Manufacturing process data collection and analytics

ABSTRACT

Techniques for comparing two or more sessions of a manufacturing process are described. In one example, a particular metric associated with execution of a manufacturing process is identified, the particular metric evaluated in a plurality of manufacturing process sessions for at least one manufacturing process. A particular session particular session from the plurality of manufacturing process sessions is selected as a baseline session, wherein at least a portion of the remaining plurality of manufacturing process sessions are to be compared to the baseline session. In a primary portion of a presentation area, a visualization of the values of the identified metric associated with the particular session are presented. In a secondary portion of the presentation area, visualizations of the values of the identified metric associated with at least a portion of the other manufacturing process sessions from the plurality of sessions is presented.

CLAIM OF PRIORITY

This application claims priority under 35 USC § 119(e) to U.S. PatentApplication Ser. No. 62/464,543, filed on Feb. 28, 2017 and also claimspriority under 35 USC § 119(e) to U.S. Patent Application Ser. No.62/466,821, filed on Mar. 3, 2017, both applications and their entirecontents are hereby incorporated by reference.

TECHNICAL FIELD

The present disclosure relates to techniques for receiving real timemanufacturing data and events from automation systems over real time andnon-real time interfaces and associating the manufacturing data andevents with sets of instructions provided to the machines for execution(e.g., numerical control (NC) code sentences). The present disclosurefurther relates to the processing of the associated manufacturingprocess data and events to provide and present interactivethree-dimensional (3D) visualizations and advanced analytics for processengineers and other users in combination with the manufacturingprocesses.

BACKGROUND

Machines used for programmed manufacturing processes include machinetools, robots, 3D printers, or any other manufacturing system operatingor controller with explicit use of 3D space. Computerized numericalcontrol, or CNC, as well as other types of controls, includingprogrammable logic controls (PLC), are commonly used in these machines.CNC is defined as the form of programmable automation, in which theprocess is controlled by the number, letters, and symbols of NC code.Other types of machines may use other types of code different than NCcode, including PLCs using language such as Relay Ladder Logic (RLL),Sequential Function charts, functional block diagrams, structured text,instructions list, and continuous function charts, among others. Theseinstructions allow for the programmable automation to be used for theoperation of the machines. In other words, a machine is controlled bythe set of instructions, e.g., an NC code or program or other format ofcode or program. In numerical control methods, the numbers form thebasic program instructions for different types of jobs. The programexecuted can change based on the job type, where each program representsa different set of code.

Many of today's programmable machines are not equipped to transmit andreceive continuous, high-frequency manufacturing data streams makingreal-time monitoring and control of these machines impossible. Further,tracing durations are limited to short durations for diagnostic purposesonly. Previous systems collected data separately and without connectionto one another, such that geospatial points, process values, andparticular instructions were collected individually without connectionsrequired for analytics.

SUMMARY

One implementations of the present disclosure is directed to receivingmanufacturing data and events over real time and non-real timeinterfaces and associating the data with one another. In one exampleimplementation, a computerized method executed by hardware processorscan be performed. The example method can comprise receiving real timedata via a real time interface of a data collection system associatedwith a manufacturing process session, the real time data associated witha counter value assigned by a precision counter associated with anautomation system. The received real time data is then stored in astorage buffer. Non-real time data is received via a non-real timeinterface of the data collection system, where the non-real time data isalso associated with a counter value assigned by the precision counterassociated with the automation system. In response to receiving thenon-real time data, the storage buffer for stored real time data issearched for data with matching counter values to the counter valueassociated with the received non-real time data. In response toidentifying stored real time data associated with a matching countervalue, the non-real time data is associated with the located real timedata based on the matching counter value. At least one data package canbe generated that includes at least one set of real time data andnon-real time data associated with matching counter values.

Implementations can optionally include one or more of the followingfeatures. In some instances, the method may further comprisetransmitting the at least one generated data package to ananalytics-based monitor and control system.

In some instances, in response to failing to identify stored real timedata associated with a matching counter value receiving the non-realtime data, the received non-real time data may be associated with anorder, where the order of the received and non-matching non-real timedata is after previously received real time data having a counter priorto that of the non-real time data and before real time data having acounter after the counter of the non-real time data.

In some instances, the storage buffer storing the real time data mayonly be searched after receiving the non-real time data and/or after astart condition associated with a particular trace is received.

In some instances, a matching counter value may be based on a countervalue that is within a predetermined number of counter increments fromanother counter value.

In some instances, generating the at least one data package is performedin response to a determination that an end event or condition isreceived via the real time data or the non-real time data.

In some instances, generating the at least one data package is performedin response to a determination that the real time data storage buffer isfull, where generating the at least one data package comprises emptyingthe real time data storage buffer in response to generating the at leastone data package.

In some instances, the precision counter comprises a counterincrementing at defined constant intervals, where the counter isexecuted at the automation system. In other instances, the precisioncounter may be executed external to the automation system.

In some instances, at least a portion of the received real time data orthe received non-real time data is ignored at the data collection systembased on a defined trace configuration associated with the monitoring ofthe manufacturing process session.

In some instances, at least a portion of the real time data comprisespositional or non-positional data for at least one component of aphysical machine associated with the manufacturing process session, datareceived from the physical machine providing information identifyingexecution operations monitored during the manufacturing process session,and sensor data related to monitored forces or position informationassociated with the manufacturing process session. In some instances, atleast a portion of the non-real time data comprises data including acurrent instruction execution at a particular time, a received userinteraction associated with a physical machine associated with themanufacturing process session, and physical machine-related eventsoccurring during the manufacturing process session.

Similar operations and processes may be performed in a system comprisingat least one process and a memory communicatively coupled to the atleast one processor where the memory stores instructions that whenexecuted cause the at least one processor to perform the operations.Further, a non-transitory computer-readable medium storing instructionswhich, when executed, cause at least one processor to perform theoperations may also be contemplated. In other words, while generallydescribed as computer implemented software embodied on tangible,non-transitory media that processes and transforms the respective data,some or all of the aspects may be computer implemented methods orfurther included in respective systems or other devices for performingthis described functionality. The details of these and other aspects andembodiments of the present disclosure are set forth in the accompanyingdrawings and the description below. Other features, objects, andadvantages of the disclosure will be apparent from the description anddrawings, and from the claims.

Another implementation of the present disclosure is directed toreceiving visualizing two-dimensional (2D) metric data in connectionwith a three-dimensional (3D) visualization data of a manufacturingprocess. In one example implementation, a computerized method executedby hardware processors can be performed. The example method can comprisepresenting a 3D visualization of machine-related data from amanufacturing process session for manufacturing a particular workpiece,where the manufacturing process is performed by a machine operating in3D, and where the machine-related data is associated with a path takenby a tool or end-effector associated with the machine and/or the machineitself during the manufacturing process session. At least one 2D dataset representing a metric associated with manufacturing process sessionis presented, and a selection of a particular 2D metric set is receivedfor presentation within the presented 3D visualization of themachine-related data. A connection between values of the 2D metric setand the machine-related data of the 3D visualization is determined, and,based on the determined connection, the selected 2D metric set isincorporated into the 3D visualization of the machine-related data.

Implementations can optionally include one or more of the followingfeatures.

In some instances, the 2D metric set can comprise a metric valueevaluated over a period of time during the manufacturing processsession. In those instances, determining the connection between valuesof the 2D metric set to the machine-related data of the 3D visualizationcan comprise connecting the metric values of particular times from the2D metric set to the visualizations of the machine-related data at thecorresponding particular times.

In some instances, the 2D metric set can comprise a metric valueevaluated over a distance traveled during the manufacturing processsession. In those instances, determining the connection between valuesof the 2D metric set to the machine-related data of the 3D visualizationcan comprise connecting the metric values associated with particulardistances traveled to the visualizations of the machine-related data atthe corresponding distances traveled.

In some instances, the machine-related data in the 3D visualization cancomprise machine-related data defining an actual path taken by a tool orend-effector associated with the machine and/or the machine itselfduring the manufacturing process session. In other instances, themachine-related data in the 3D visualization can comprisemachine-related data defining a path expected to be taken by the pathtaken by a tool or end-effector associated with the machine and/or themachine itself based on a set of movement instructions provided to themachine during the manufacturing process session.

In some instances, the selected 2D metric can comprise a measuredparameter captured during the manufacturing process session or acalculated parameter derived from one or more measured parameterscaptured during the manufacturing process session. In other instances,the selected 2D metric can comprise a calculated value determined bycomparing an actual path taken by the machine during the manufacturingprocess session to a path expected to be taken by the machine based on aset of movement instructions provided to the machine during themanufacturing process session.

In some instances, incorporating the selected 2D metric set into the 3Dvisualization of the machine-related data can comprise presenting theselected 2D metric set on the 3D visualization. In those instances, a 3Dcomputer-aided design (CAD) visualization of the workpiece can bepresented in connection with the 3D visualization of the machine-relateddata, wherein the selected 2D metric set is presented on the 3Dvisualization at an angle based on an orientation relative to presentedworkpiece. In those instances, in response to a rotation of theworkpiece, the presentation of the selected 2D metric set incorporatedin the 3D visualization is rotated in connection with the rotation ofthe workpiece.

In some instances, at least a portion of the selected 2D metric set,when incorporated into the 3D visualization, is presented in a reducedscale.

In some instances, the 3D visualization is presented in a firstpresentation area in a user interface, and, at least prior to selection,the at least one 2D metric data sets is presented in a secondpresentation area in the user interface separate from the firstpresentation area.

Similar operations and processes may be performed in a system comprisingat least one process and a memory communicatively coupled to the atleast one processor where the memory stores instructions that whenexecuted cause the at least one processor to perform the operations.Further, a non-transitory computer-readable medium storing instructionswhich, when executed, cause at least one processor to perform theoperations may also be contemplated. In other words, while generallydescribed as computer implemented software embodied on tangible,non-transitory media that processes and transforms the respective data,some or all of the aspects may be computer implemented methods orfurther included in respective systems or other devices for performingthis described functionality. The details of these and other aspects andembodiments of the present disclosure are set forth in the accompanyingdrawings and the description below. Other features, objects, andadvantages of the disclosure will be apparent from the description anddrawings, and from the claims.

Another implementation of the present disclosure is directed to viewinga plurality of related presentation areas linked to a set ofmachine-related data. In one example implementation, a computerizedmethod executed by hardware processors can be performed. The examplemethod can comprise presenting a machine-related data set associatedwith a manufacturing process session performed by a machine formanufacturing a particular workpiece, the machine-related data setrepresenting a common underlying data set presented in a plurality ofpresentation areas, where each presentation area is associated with aseparate view on the machine-related data set. Within a particular oneof the presentation areas, a selection of a particular group of datapoints is identified and the presentation of the particular presentationarea is updated based on the selected group of data points. Referencevalues associated with the common underlying data set are identifiedbased on the selected group of data points. For each of the otherpresentation areas, a particular set of data included in the particularother presentation area corresponding to the identified reference valuesis identified, the particular other presentation area is updated basedon the identified particular set of data corresponding to the identifiedreference values.

Implementations can optionally include one or more of the followingfeatures.

In some instances, the plurality of presentation areas includes at leasttwo of a three-dimensional (3D) visualization of a path taken by a toolor end-effector associated with the machine and/or the machine itself,at least one two-dimensional (2D) data set representing a metricassociated with the manufacturing process session, a set of instructionsprovided to the machine during the manufacturing process session, a setof critical regions, and a time slider.

In those instances, the reference values associated with the commonunderlying data set comprise a set of time values associated with themanufacturing process session.

Alternatively, in those instances, the reference values associated withthe common underlying data set comprise a distance traveled on the pathtaken by a tool or end-effector associated with the machine and/or themachine itself, where locations traveled on the path are associated withreference time values.

Alternatively, in those instances, the reference values associated withthe common underlying data set comprise a spatial reference associatedwith the manufacturing process session.

In some instances, the selection of the particular group of data pointscomprises a selection of a time range in the particular presentationarea.

In some instances, the selection of the particular group of data pointscomprises a selection of a spatial area within the 3D visualization ofthe path taken by a tool or end-effector associated with the machineand/or the machine itself, wherein the particular group of data pointscomprises a non-sequential set of data points.

In some instances, the selection of the particular group of data pointscomprises a selection of at least one instruction from the set ofinstructions provided to the machine during the manufacturing processsession.

In some instances, the path taken by a tool or end-effector associatedwith the machine and/or the machine itself comprises an actual pathtaken by the machine during the manufacturing process session or a pathexpected taken by a tool or end-effector associated with the machineand/or the machine itself during the manufacturing process session basedon the set of instructions executed by the machine.

In some instances, identifying the reference values associated with thecommon underlying data set based on the selected group of data points inthe particular presentation area comprises accessing metadata associatedwith the particular presentation area to identify associations to thecommon underlying data set.

In some instances, the selection of the selected group of data points isperformed automatically based on an identification of at least onecritical region associated with the particular presentation area, wherethe critical region is associated with values exceeding a definedthreshold value.

Similar operations and processes may be performed in a system comprisingat least one process and a memory communicatively coupled to the atleast one processor where the memory stores instructions that whenexecuted cause the at least one processor to perform the operations.Further, a non-transitory computer-readable medium storing instructionswhich, when executed, cause at least one processor to perform theoperations may also be contemplated. In other words, while generallydescribed as computer implemented software embodied on tangible,non-transitory media that processes and transforms the respective data,some or all of the aspects may be computer implemented methods orfurther included in respective systems or other devices for performingthis described functionality. The details of these and other aspects andembodiments of the present disclosure are set forth in the accompanyingdrawings and the description below. Other features, objects, andadvantages of the disclosure will be apparent from the description anddrawings, and from the claims.

Another implementation of the present disclosure is directed tocomparing two or more sessions of a manufacturing process are described.In one example implementation, a computerized method executed byhardware processors can be performed. The example method can compriseidentifying a particular metric associated with execution of amanufacturing process, the particular metric evaluated in a plurality ofmanufacturing process sessions for at least one manufacturing process. Aparticular session from the plurality of manufacturing process sessionsis identified as a baseline session, wherein at least a portion of theremaining plurality of manufacturing process sessions are to be comparedto the baseline session. In a primary portion of the presentation area,a visualization of the values of the identified metric associated withthe identified particular session is presented. In a secondary portionof the presentation area, visualizations of the values of the identifiedmetric associated with at least a portion of the other manufacturingprocess sessions from the plurality of sessions are provided.

Implementations can optionally include one or more of the followingfeatures.

In some instances, in response to identifying the particular sessionfrom the plurality of manufacturing process sessions as a baselinesession, at least one of the other manufacturing process sessions areautomatically normalized to the baseline session based on acharacteristic of the identified particular session. In some of theseinstances, the characteristic of the identified particular sessioncomprises a time period over which the identified particular session wasperformed. In some of these instances, normalizing the at least one ofthe other manufacturing process sessions comprises compressing orexpanding visualizations of the at least one of the other manufacturingprocess session over the time period of the identified particularsession, where the at least one of the other manufacturing processsessions was performed over a different time period than the identifiedparticular session.

Alternatively, the characteristic of the identified particular sessioncomprises a traveled distance of the machine performing themanufacturing process.

In some instances, the identifying the particular session from theplurality of manufacturing process sessions as a baseline sessioncomprises initially presenting at least a portion of the plurality ofmanufacturing process sessions via a user interface and receiving, viathe user interface, a selection of the particular session by a userassociated with the user interface. In those instances, the methodfurther comprises receiving, via the user interface, a selection of anew session from the presented portion of the other manufacturingprocess sessions as a new baseline session. In response to receiving theselection of the new session, a visualization of the values of theidentified metric associated with the selected new session are presentedin the primary portion of a presentation area. In the secondary portionof the presentation area, visualizations of the values of the identifiedmetric associated with at least a portion of the other manufacturingprocess sessions from the plurality of sessions are presented.

In some instances, the visualization presented in the primary portion ofthe presentation area is at a first level of detail and thevisualizations presented in the secondary portion of the presentationarea are at a second level of detail, wherein the first level of detailis a relatively higher level of detail than the second level of detail.

In some instances, at least a portion of manufacturing process sessionsin the plurality of manufacturing process sessions are performed ondifferent machines performing a common manufacturing process to machinea workpiece. In the same or other instances, at least a portion ofmanufacturing process sessions in the plurality of manufacturing processsessions are performed on different machines performing differentmanufacturing processes, where the different manufacturing processeseach machine a common workpiece.

In some instances, the identified particular session is automaticallyidentified initially, where the identified particular session comprisesa simulated session based on averaged metric values of at least aportion of the plurality of manufacturing process sessions.

Similar operations and processes may be performed in a system comprisingat least one process and a memory communicatively coupled to the atleast one processor where the memory stores instructions that whenexecuted cause the at least one processor to perform the operations.Further, a non-transitory computer-readable medium storing instructionswhich, when executed, cause at least one processor to perform theoperations may also be contemplated. In other words, while generallydescribed as computer implemented software embodied on tangible,non-transitory media that processes and transforms the respective data,some or all of the aspects may be computer implemented methods orfurther included in respective systems or other devices for performingthis described functionality. The details of these and other aspects andembodiments of the present disclosure are set forth in the accompanyingdrawings and the description below. Other features, objects, andadvantages of the disclosure will be apparent from the description anddrawings, and from the claims.

DESCRIPTION OF DRAWINGS

FIGS. 1-3 are a block diagram illustrating an example system 100 forcontextually associating the data from various production sessions,generating a common set of data for visualizing the session's actual andexpected operations, and for analytically evaluating one or multiplesessions associated with a particular workpiece or aggregations ofworkpieces.

FIG. 4 is a swimlane diagram illustration of three example tracingscenarios.

FIG. 5 is an example flowchart of a process for receiving real andnon-real time data and performing the contextual cutting of the receiveddata.

FIG. 6 is an example flowchart of a process for transforming the data ofboth expected and actual data sets into the common coordinate system, aswell as determinations of potential critical regions based on contouringerrors and other potential issues.

FIG. 7 illustrates a flowchart of an example process for displayingtwo-dimensional (2D) metric data associated with a three-dimensional(3D) visualization of a manufacturing process session onto the 3Dvisualization.

FIG. 8 illustrates a flowchart of an example process for providing amultiple window or presentation area presentation of different views ona common underlying set of data associated with a manufacturing processsession.

FIG. 9 illustrates a flowchart of an example process for performing apattern-based benchmarking process based on various metrics measuredand/or calculated during various manufacturing process sessions.

FIG. 10 presents an example view of a user interface where a 3Dvisualization is presented.

FIG. 11 presents an example illustration of a zoomed in portion of a 3Dvisualization.

FIGS. 12A-B and 13A-B illustrate example interactions provided by themultiple window or presentation area presentation of different views ona common underlying set of data associated with a manufacturing processsession.

FIGS. 14A-14D illustrate example visualizations and interactionsprovided by the pattern-based benchmarking solution.

DETAILED DESCRIPTION

The present disclosure relates to the correlation and combination ofdata from across machines (e.g., in a production or manufacturingfacility or location, among others), tools, end effectors, raw materialand their related systems, controllers, and associated sensors in amanufacturing and/or industrial system including operations and ambientenvironmental conditions. Further, this disclosure describes a group ofrelated solutions in providing visualizations and analytics related tothe operations, execution, and comparison of the operations of suchmachines via intelligent and advanced operations and analyticalcapabilities.

Workpieces are components manufactured during subtractive manufacturingprocesses in which 3D objects are constructed successively by cutting,shaping or other forms of deformation. Conversely, workpiece can also bemanufactured by additive or joining processes. In the presentdisclosure, the subject of a particular manufacturing process is definedas a workpiece.

In general, the systems described herein allows for thecontextualization, evaluation, and visualization of monitored dataassociated with one or more sessions and operations of a particularmachine, tool, or other manufacturing component operating and/or movingin a three-dimensional (3D) space. Such manufacturing machines mayinclude CNC controlled devices (e.g., multi-axis milling machines,robots, 3D printers, each with and without PLCs, etc.), probe andmeasurement systems, PLC-based assembly and pick and place-enabledmachines, other PLC-based systems, as well as machines equipped withadditional control and supervision systems, e.g., human-machineinterfaces (HMIs) and additional sensors. The additional sensors mayinclude, but are not limited to, laser interferometers, intelligenttools, ambience sensors, messengers, and other sensors capable ofcapturing and reporting on operations as performed by the monitoredmanufacturing machines or any of its components or subsystems, such as atool, jigs, or the workpiece itself. The manufacturing machines mayinteract with other systems of the same functionality, or may be part ofa larger construct or machine line where a series of operations areperformed over or by several different manufacturing machines. The CNCdevices, industrial robots, and other physical machines operate in a 3Dspace, with portions and/or components of the machine moving indifferent directions and/or on different axes when operational.

The velocity of the system enables in-process workpiece qualityfeatures, such as dimensional/geometric error assessment and detectionof root causes for detected issues. By correlating—either via userinteraction or algorithmically—relevant variables (e.g. raw material,manufacturing data from preceding operations, ambient and other data)and monitored data related to the manufacturing process (e.g., amachining process, a workpiece generation process, etc.), qualityfeatures such as inaccurate workpiece dimensions and poor surfaceintegrity can be computed, visualized (and eventually corrected) such aschatter and other relevant calculations and comparisons. The system usesprimary, e.g. drives bus, or aggregate (provided by the numerical andother controllers) encoder data for position, velocity, and direction,as well as other sensor signals/process variables and events fromconnected controllers. Other signals and process variables are alsoobtained in addition to the primary encoder data. The system usesmeasurement system data for calibration and validation of itsalgorithms, and can provide visualizations of the actual and expecteddata (e.g., expected actions/operations based on the commanded set ofinstructions), as well as one or more of the measured or calculatedvalues. The resulting data and visualizations can also provide clear anddetailed information regarding a particular machine's health and that ofthe processes executed by those machines.

The operations of particular machines and their tools, effectors, orother moving components or subsystems can be directed by a particularset of instructions, which in some instances may be numerical control(NC) code and/or programs, as well as other types of code, whereappropriate. The set of instructions is provided to the machines andother components, and during execution the set of instructions isinterpreted and interpolated, and may be associated with an expectedoutput, such as finer or commanded values sent to various sub-systems,such as motors, drives and lasers. The expected output may be associatedwith machine or subsystem data, operations, identifiers, and/or sensordata from sensors associated with the machine or subsystem. Expectedoutput can also include a relative or absolute location of a part orcomponent of a machine, an operational state of the machine orcollection of machines, a workpiece identifier, or whether a particularpart or the whole of the machine is on or off, among others. Based onthe expected output, particular expected actions or operations of themachine or subsystem, including the machine's (or it's associated toolsor other components) expected movements, can be identified that canlater be compared to the actual output of the machine. Sensor andmachine data associated with the actual execution or output of theparticular set of instructions, e.g. NC code, when executed is capturedby the machine, associated sensors, and other information-capturingdevices, components, or elements. This information can be mappedtogether to identify and define a correlation between the two data sets,allowing the comparisons and evaluations of the expected and actualvalues to be performed and visualized for users.

This mapping between the two (i.e., expected and actual actions),provides extensive insight previously unavailable. Data provided by themachine of the actual actions performed can provide some analyticalresult, but does not allow for a comparison against the expected data.When combined with the expected actions, significant insight can beobtained in order to provide a valuable comparison and evaluation of therelative performance in the actual instance as compared to the expectedinstance, including further insight as to potential bottlenecks, issues,or other problems that may occur in the manufacturing arena.

In a first solution, various machine-related information is received atdifferent times and from various locations, e.g., sources, subsystemswith controllers, sensors, etc. Receiving at different times may meanthat data originates from sources operating in their own time frame orother information not available in a real-time basis, such as jitter(i.e., irregularity of electronic signals), latency (i.e., time intervalbetween stimulation and response) of the source emitting the data,and/or events or other inputs that are not directly timed to theoperations of the system. The information may be monitored, received,and/or caused by a controller or other automation system associated withthe machine and its moving components performing the manufacturingprocess. However, prior solutions were unable to coordinate and linksuch information when incoming data and information was received orgenerated at different times, such as where at least some of theinformation was associated with a transformation or additionalcomputation before being sent to a transactional or other datacollection system. The first solution offers a solution to allowcontextual linking and association of data as it is received throughvarious inputs. To do so, a high precision counter incrementing with ahigh frequency (e.g., at or near the highest frequency of incominginformation, or at another suitable frequency) is introduced to thesystem. The frequency of the counter can be any suitable frequency, andcan include the highest unit of frequency needed to correlate data(e.g., based on the highest frequency clock or timing betweendeterministic data streams. The high precision counter may be includedat an automation system or it may itself be an external signal receivedat the controller or machine. As inputs are received from the machineand the related sensors, the current counter value of the high precisioncounter is associated with the received or identified input. Theautomation system or controller can provide the input with the countervalue to a further system for processing and association. In someinstances, the automation system may act as a pass-through for some ofthe data to allow for real-time or near-real-time provision of the datato the data collection system. Other data, however, may be processed atthe automation system and therefore be delayed in the sending of thedata back to the data collection system, such that the data is providedto the collection system in a non-real-time manner. Without the countervalues, such data may be difficult or impossible to link or associatewith the real-time data already received at the collection system, suchthat future analytics and visualizations may not be possible. Data maybe held at the data collection system until an end event or other signalindicating the end of desired session recording or tracing. The end ofthe tracing is not necessarily equal to the end of the manufacturing ofthe workpiece. This is important as control mechanisms described hereinare utilized to control (and thereby reduce) the data volumesoriginating from the machine/sensors/controllers which are beingforwarded to the data collection system. At that time, or at any othersuitable time, including prior to an end event or determination, thedata can be contextualized to other data based on the common countervalues associated with the received real-time and non-real-time data.Once all data with a counter prior to the end event or end-associatedsignal is contextualized, the set of data related to the session can beprovided to a data sink or analytic system for further modification andevaluation.

A second solution described herein relates to the 3D visualization ofthe operations of the manufacturing machine monitored and managed by thedisclosed system. For example, the present solution allows for adetailed 3D visualization of the operations performed by themanufacturing machine or subsystem, as well as or instead the operationsthat are instructed to be performed by the manufacturing machine—alwaysassociated with the workpiece being manufactured. In some instances, thevisualization can allow the actual operations performed to be comparedto the operations as they were instructed, thereby allowing users suchas process engineers to immediately identify potential variations and/orissues with the actual operations of the monitored machine. To generatethe visualizations, the contextualized sets of data, which can includeinformation describing the actual path taken by a tool or end-effectorassociated with the machine or the machine, e.g. machine tool,industrial robot, itself, the instructed path provided to the machine, aparticular set of instructions and/or instructions associated with theinstructed path, and other monitored and/or calculated information, canbe independently transformed into a common underlying coordinate system.As the machine and its instructions may be based on differing coordinatesystems (e.g., statically and dynamically changing coordinate systemswithin and across manufacturing sessions), the linking of the coordinatesystems allow the received data to be placed into a common coordinatesystem, thereby allowing the data to be mapped in a uniform manner.Without the common underlying coordinate system, illustrations of thevarious data sets may not be connected, and therefore unable toillustrate the proper comparisons on a single coordinate system. Whengenerating the visualization, a 3D computer-aided design (CAD) model ofa workpiece, may be included in the visualization to show how theworkpiece is being manufactured in reference to the actual path orcommanded/expected path of the tool or end-effector associated with themachine or the machine, e.g. machine tool, industrial robot, itself. The3D visualization may be interactive, such that users viewing thevisualization may be able to manipulate and rotate the visualization ofthe paths and the workpiece. In addition to the visualization itself, aseries of calculations and determinations from the actual and/orexpected values can be derived. Those particular evaluations can becalculated and presented along with or in a separate visualization ofthe actual and expected paths or actions of the machine(s) or componentsthereof. In some implementations, the evaluations and calculationsassociated with the derived values can be presented in separate windows,panes, or areas of a user interface(s).

In a third solution, one or more two-dimensional (2D) sets of dataassociated with the various calculations may be obtained, but which werepreviously only able to be presented outside of the 3D presentation ofthe data—generally outside of the 3D canvas in which the 3Dvisualization is presented. Manual comparisons and analysis between the3D visualization and the 2D representations were time-consuming forusers and difficult to correlate. The present solution solves thesedeficiencies by allowing 2D metric data to be rendered on the 3D canvasassociated with the 3D visualization of the data. By doing so, theportions of a particular path or spatial reference associated withcalculated issues may be determined visually within the 3Dvisualization. In some instances, particular thresholds for calculatedor monitored metrics may be identified as critical areas, that is, wherethe calculated and/or monitored values of the metric exceed or fallbelow an identified value (e.g., a raw percentage or value, a number ofstandard deviations from an average value, etc.). Using the presentationof the 2D metric data on the 3D visualization, particular areasassociated with these critical areas can be immediately identified witha unique or distinguishing presentation. By visualizing and potentiallylabeling these areas over the 3D visualization, clear contextualinsights may be gained. Using tools similar to those provided by the 3Dvisualization, users can receive a macro view of where errors haveoccurred or can focus on a particular section of the 3D model. Inassociated windows or as an annotated presentation, a list of eventsassociated with particular critical regions can be provided on oradjacent to the illustration. In response to a user input selecting aparticular critical region, the visualization can be updated to a timerange and/or a path traveled in association with the error. In someinstances, the underlying NC code or set of instructions presented in anadjacent window may be updated to show the corresponding section of thecode and allow for immediate understanding of the context of the issues.

A fourth solution described herein relates to the presentation ofrelated data in a single screen or visualization, wherein the relatedwindows within the screen present data, calculations, and input linkedto a common underlying data set, where the underlying data set (e.g.,state machine) connects the various values and presented informationwithin the related windows. In one example, the solution is called afour-way interaction solution, although any number of related windows orpresentations may be used. In one implementation, windows may include apresentation of the 3D visualization, a presentation of at least one 2Dmetric or 2D metric data, particular set of instructions, e.g. NC codeassociated with the actual and expected paths of the manufacturingmachine, and a slider indicating a particular time range and/or pathtraveled during a particular session of the manufacturing process.

In general, the four-way interaction allows users to interact withvarious aspects of the underlying data to present and updatecontextually-associated portions of the data via (e.g., four) differentinterconnected user interface controls to analyze different parametersand navigate portions of time and path traveled (e.g., total distancetraveled, spatial reference, etc.) to obtain insights from the machine,which would otherwise have to be manually pulled and manually correlatedfrom historical data. Some implementations may include fewer or morethan four interconnected user interface controls. Through interactionswith any of the available user interface portion (e.g., the differentwindows or areas), a connection to the underlying information isdetermined and identified. In response to a selection of a particulararea, range, action, or point within one of the interfaces, acorresponding portion in each of the other interconnected windows isdetermined. Once determined, the presentations of those areas areupdated to allow the corresponding views to be presented and madeavailable to the user in connection with the selected portion of thefirst area in which the initial interaction occurred.

As noted, a particular implementation may include a time slider, aparticular instructions selection box, e.g. NC code, an interactive 3-Dvisualization of the paths taken by tool or end-effector associated withthe machine or the machine, e.g. machine tool, industrial robot, itself(and optionally an image of the workpiece being worked on ormanufactured), and a 2-D chart region providing metric calculations andother detected or calculated parameters during a session. The timeslider can allow users to slide or adjust each end of an interactivetimeline in order to view a smaller portion of time (e.g., at a higherresolution or frequency of the underlying data) within a particularsession. As the user interacts with the slider, an exact time at whichthe slider is positioned may be presented. When a period of time ischosen, corresponding data from the other user interfaces areautomatically selected for viewing and comparison within that period oftime based on the connection between the common underlying data set andother presentations. In some instances, the 3D visualization may bere-rendered to reflect the portions of the machine's activities thatoccurred during the time period, while the instructions selection boxpresents code, e.g. NC code, provided and/or executed corresponding tothe selected time period. Any active 2D charts can be re-rendered forthe time period to be shown, or alternatively, to highlight or otherwiseemphasize the selected time period within the entirety of the session'sdata. The instructions selection area, e.g. displaying NC code, providesan interactive listing of the code associated with the actions performedby the machine. The instruction code selection area allows users tohighlight a portion of code (e.g., a sequential or non-sequential set ofparticular instructions or commands), to focus on and/or to select. Theselected code can be associated with a particular time or portion of theactivity performed by the monitored machine. If any errors or criticalregions are associated with particular portions of the instruction code,such portions may be highlighted in red or provided another similaremphasis or indication. Similar to time slider adjustments, when asection of code is chosen, corresponding data from the other interfacesare automatically identified as being associated with the selected codebased on the code's connection to the common underlying data, with theother interfaces being updated and presented for viewing and furtherinvestigation based on the selection. To do so, the instruction codeselected can be associated with a particular time or time period. Inresponse to the selected code, the time period or corresponding portionstraveled may be identified and provided to each of the relatedselectable areas, where those areas are then updated based on thecorresponding time period or path traveled. In some instances, aseparate structure may associate times to particular instruction code,such that when a particular portion of the instruction code is selected,the associated time can be determined based on the data in thestructure.

A fifth solution described herein relates to the comparison of multiplesessions of a particular manufacturing process for a single machine ormultiple machines.

Previously, cross-session comparison was not possible as machine processdata was not captured in a meaningful or consistent way, and temporalinternal controller data was overwritten. Data from involved machineswas also not visualized, so manual analysis was time consuming anddifficult.

The solution described herein provides a pattern-based benchmarking toolwhere users are able to visualize digitized data from multiple sessionsof production such that users can easily identify ideal configurationsor determine shifts across session data sets which are significant tomonitor and control process or product quality parameters based on acomparative visualization of the sessions. The pattern-basedbenchmarking process can allow users to identify an ideal or preferredsession to set as a baseline comparison. Using a verified or selectedbaseline session, other sessions can quickly be compared to the baselineto identify errors, outliers, and/or relative performance comparisons.

Specifically, pattern-based benchmarking allows users to comparemultiple sessions of production side-by-side in order to easily identifypatterns of error. The design visualizes data from each session in, forexample, the form of a line graph or timeline (e.g., the 2D presentationof metrics) for the user to analyze. Errors or issues that areidentified as critical regions (e.g., above or below a default,automatic, or user-defined threshold) may be highlighted in red orotherwise distinguished. Users are able to identify an ideal session(e.g., the ideal session may be based on single sessions or multiplesessions or subsets thereof, a group of sessions, or a statisticallygenerated example or optimal session, among others) during their review,select (e.g., via drag and drop, double-click, or other suitable UIinteraction) that session as a baseline session, and generate acomparison of the selected baseline session against which all othersessions will then be benchmarked for quality assurance. In someinstances, the session may not be an ideal session, but rather a sessionidentified for another reason, including a desire to review the sessionin context with other sessions, as a starting point in comparingmultiple sessions, or for a relative view of a session's results and/ordata.

Pattern-based benchmarking may be performed using the 2D chart selectionarea. Further, because multiple sessions may be reviewed, the sessionsneed not be performed by the same machine. For example, multiplesessions may represent actions performed to generate a similar workpieceacross different manufacturing systems each generating the workpiece viaa different solution, order, or instructions, or via the same order andinstructions using similar, but different machines. Alternatively, thesessions may be associated with the same manufacturing system wheredifferent instructions are used to produce the identical workpiece.Where the sessions differ in setup, the multiple sessions must benormalized over time or over the execution of the operations in order tocompare execution with the baseline selection. Upon normalization, asimilar comparison can be performed to accurately represent and evaluatethe various sessions.

The pattern-based benchmarking can be used to compare execution ascompared to particular thresholds that have been set in connection withat least one of the session data, and to use the thresholds to derivevisual patterns across sessions for error detection and benchmarking.For example, if a particular contouring error threshold is set in afirst session selected as the benchmark baseline, the threshold can bepropagated down to the remaining sessions and used to evaluate arelative performance. When a new baseline is set, the baseline graph isupdated with the selected session and a new normalization process can beperformed in order to provide an in-kind illustration in the set ofsessions.

The pattern-based benchmarking can be used by users to iteratively finda manually optimized context for future execution to produce workpiecesoptimally based on implicit/explicit understanding of relevant systembehavior affecting the same. For similar systems, problems or issueswith production and manufacturing can be identified based on errors orbehaviors seen across a plurality of other systems, increasing theknowledge and expectations for existing and modified systems.

Turning to the illustrated implementation, FIGS. 1-3 comprise a blockdiagram illustrating an example system 100 for contextually associatingthe data from various production sessions, generating a common set ofdata for visualizing the session's actual and expected operations, andfor analytically evaluating one or multiple sessions associated with aparticular workpiece or aggregations of workpieces. System 100 is asingle example of a possible implementation, with alternatives,additions, and modifications possible for performing some or all of thedescribed operations and functionality. As illustrated in FIGS. 1-3,system 100 is associated with a system capable of sharing andcommunicating information across devices and systems (e.g., client 105,analytics-based monitor and control system 130 (or “analytics system130”), data collection system 202), an automation system or controller302, and one or more physical machines (machines, robots, 3D printers)370 (along with any associated sensors 375 and data sources 380, amongothers) via network 190). In some instances, only a single physicalmachine 370 may be associated with the overall system 100, while inothers, a plurality of physical machine 370 may be associated with thesystem 100. Further, a plurality of automation systems 302 may beassociated with one or more physical machine 370, including where asingle automation system 302 is associated with each physical machine370 included in the system 100. Still further, each automation system302 may be associated with a particular data collection system 202. Insome instances, a single data collection system 202 may be associatedwith a plurality of automation systems 302. Although components areshown individually, in some implementations, the functionality of two ormore components, systems, or servers may be provided by a singlecomponent, system, or server. Further, additional components may beincluded in alternative implementations that perform at least a part ofthe functions of the illustrated components.

As used in the present disclosure, the term “computer” is intended toencompass any suitable processing device. For example, client 105,analytics-based monitor and control system 130, data collection system202, and automation system 302 may be any computer or processing devicesuch as, for example, a blade server, general-purpose personal computer(PC), Mac®, workstation, UNIX-based workstation, embedded system or anyother suitable device. Moreover, although FIGS. 1-3 illustrateparticular components as a single element, those components may beimplemented using a single system or more than those illustrated, aswell as computers other than servers, including a server pool orvariations that include distributed computing. In other words, thepresent disclosure contemplates computers other than general purposecomputers, as well as computers without conventional operating systems.Client 105 may be any system which can request data and/or interact withthe analytics-based monitor and control system 130 and, in some cases,one or more of the data collection system 202 and/or the automationsystem 302, as well as the underlying physical machine 370. The client105, in some instances, may be a desktop system, a client terminal, orany other suitable device, including a mobile device, such as asmartphone, tablet, smartwatch, or any other mobile computing device. Ingeneral, each illustrated component may be adapted to execute anysuitable operating system, including Linux, UNIX, Windows, Mac OS®,Java™, Android™, Windows Phone OS, or iOS™, any real-time OS amongothers.

To begin the overall description, the physical machine 370 is initiallyidentified as the basis of the system, where the physical machine 370represents a manufacturing device of any suitable type. Suchmanufacturing machines may include CNC controlled devices (e.g.,multi-axis milling machines, robots, 3D printers, each with and withoutPLCs, etc.), probe and measurement systems, PLC-based assembly and pickand place-enabled machines, other PLC-based systems, as well as machinesequipped with additional control and supervision systems, e.g.,human-machine interfaces (HMIs) and additional sensors. Examples of thephysical machine 370 may include a CNC tool loaded in a spindle or anend effector for robots and 3D printers, The physical machine 370, asillustrated, may be any suitable machine, industrial robot, or 3Dprinter, or other manufacturing machines operating in three dimensions,capable of interacting with a workpiece (i.e., the items or productbeing worked on and/or manufactured via operations of the physicalmachine 370) in a 3D space, along one or more axes of movement, linearor rotational, with portions and/or components of each physical machine370 moving in different directions and axes when operational. In someinstances, the physical machine 370 may act independently of otherdevices or tools, while in others the physical machine 370 may be partof an assembly line or other set of connected physical machine 370 orother machines, where the workpiece is passed or transferred from onemanufacturing step and/or tool to another. The physical machine 370 mayreceive instructions from the automation system 302 (or controller),where the instructions are provided in any suitable format. Fordiscussion and example purposes, the instructions described herein maybe provided in a numerical control (NC) format, although other formatsfor the set of instructions may be used, where appropriate, such as aPLC format associated with, for example, assembly to end effectors ofrobot tools, among others. Thus, for this illustrated implementation,the instructions may be instructions sets, e.g. NC code 353 or NCprograms that provide machine-level instructions for the operations of aparticular process. The physical machine 370 can execute theinstructions as provided, with internal information about the executionof those instructions being captured by the physical machine 370. Thephysical machine 370 can then provide information about the actualactions performed back to the automation system 302 for furtherprocessing and collection.

Additional sensors 375 may be associated with and may monitor theoperations of a particular physical machine 370, a component of thephysical machine 370, associated tools and jigs, the workpiece beingmanufactured, and the position of any of these or other parts of themanufacturing process, to provide additional insight and informationabout the actual execution of one or more manufacturing sessions. Aparticular sensor 375 may include, in some instances, at least onesensor plus at least one controller capable of transmitting sensor databased on automation and process protocols, in some instances via network190. The additional sensors may include, but are not limited to, laserinterferometers, ambience sensors (e.g., temperature sensors, acousticsensors, pressure sensors, vibration sensors etc.), cameras (infrared,visible light, heat, etc.), messengers, and other suitable sensors,where the sensors 375 are capable of capturing and reporting onoperations on the particular physical machine 370 and the machine 370itself. The sensors 375 can provide further information than may beavailable from the physical machine 370 alone, and may be used toenhance the data set obtained by the automation system 302 from themachine 370. Still further, one or more related data sources 380 mayalso be added to the information collected by the automation system 302,including information received from particular HMIs (e.g., panels,keyboards, buttons, touch-screen interfaces, biometric input devices,etc.) associated with the physical machine 370, information on otheruser interactions, user inputs, and/or other events, informationexternal to the particular monitored physical machine 370, as well asother relevant data. Data sources 380 may include the HMIs associatedwith the physical machine 370, sources of data providing context orinformation associated with or providing reference to particular actionsand activities associated with the manufacturing process, or any otherexternal sources of data or information potentially incorporated intothe data captured in relation to particular manufacturing processes andoperations.

As illustrated, information may be provided by the physical machine 370,the sensors 375, and the data sources 380 via network 190. Network 190facilitates wireless or wireline communications between the componentsof the environment 100 (e.g., between the physical machines 370 and theautomation system 302, between the automation system 302 and the datacollection system 202, between the data collection system 202 and theanalytics-based monitor and control system 130, and between theanalytics-based monitor and control system 130 and client 105, amongothers), as well as with any other local or remote computer, such asadditional mobile devices, clients, servers, tools, and/or other devicescommunicably coupled to network 190, including those not illustrated inFIG. 1-3. In the illustrated environment, the network 190 is depicted asa single network connecting multiple components, although network 190may be comprised of more than one network without departing from thescope of this disclosure, so long as at least a portion of the network190 may facilitate communications between senders and recipients. Insome instances, one or more of the illustrated components (e.g., theanalytics-based monitor and control system 130) may be included withinnetwork 190 as one or more cloud-based services or operations. Thenetwork 190 may be all or a portion of an enterprise or secured network,while in another instance, at least a portion of the network 190 mayrepresent a connection to the Internet. In some instances, a portion ofthe network 190 may be a virtual private network (VPN). Further, all ora portion of the network 190 can comprise either a wireline or wirelesslink and/or a combination thereof. Example wireless links may include802.11a/b/g/n/ac, 802.20, WiMax, LTE, and/or any other appropriatewireless link. In other words, the network 190 encompasses any internalor external network, networks, sub-network, or combination thereofoperable to facilitate communications between various computingcomponents inside and outside the illustrated environment 100. Thenetwork 190 may communicate, for example, Internet Protocol (IP)packets, Frame Relay frames, Asynchronous Transfer Mode (ATM) cells,voice, video, data, and other suitable information between networkaddresses. The network 190 may also include one or more local areanetworks (LANs), radio access networks (RANs), metropolitan areanetworks (MANs), wide area networks (WANs), all or a portion of theInternet, and/or any other communication system or systems at one ormore locations. In some instances, a portion of network 190 may be alocal connection (e.g., between the physical machines 370, sensors 375and the automation system 302) while another portion of the network 190may be a connection to the Internet or to an Intranet-based system(e.g., between client 105 and analytics-based monitor and control system130). In the present example, communications may be performed over anysuitable connection, including Ethernet, a PROFINET connection, awireless connection, or any other suitable standard. The PROFINET(Process Field Network) standard is the open industrial Ethernetstandard of the PROFIBUS (Process Field Bus) user organization (PNO) forautomation. PROFINET uses TCP/IP and IT standards, is real-timeEthernet-capable, and enables the integration of fieldbus systems. Otherautomation protocols may also be used for communications in the system,including process automation protocols such as fieldbus protocols.

As described, information and data from each physical machine 370 andits associated sensors 375 (as well as any other data sources 380) areprovided to the automation system 302 via network 190. The signals fromthose components can be provided to specific inbound signal interfaces326 of the automation system 302 (e.g., signal interfaces 329 formachine tool-communications and signal interfaces 332 for sensor- anddata source-related data) associated with a data management module 323of the automation system 302. Data received from related data sources380 may include event indications from one or more human interactions(e.g., by a machine or tool operator (not shown)), an indication ofparticular operations performed by or instructions executed by themachine 370 associated with a start or end event of a manufacturingprocess for a session, specific location information associated with amoving tool, workpiece or jig in case of machine tool, an end effectorin case of robots, as well as other suitable information associated withthe actual execution of the instructions provided by the automationsystem 302.

As described herein, the automation system 302 may include or may be acontroller of its associated physical machines 370, providing theinstructions to be executed to the physical machines 370. In someinstances, the automation system 302 may be included within, embeddedon, or a part of a particular physical machine 370. The automationsystem 302 can provide the operation instructions (i.e., the contents orcode of instructions sets, e.g. NC program 353) to the physical machine370 of type machine tool for execution. The automation system 302 mayinclude a machine management module 311 which performs tasks associatedwith controlling the one or more physical machines 370. As illustrated,the machine management module 311 may include a numeric controller 314,a programmable logic controller (PLC) 317, and a shared memory 320. Insome instances, the machine management module 311 may only include oneof the numeric controller 314 or the PLC 317, such as where the physicalmachine 370 is a robot (only the PLC 317 may be needed) or where thephysical machine 370 is a purely NC-based machine tool (only the numericcontroller 314 may be needed). The shared memory 314 is used to exchangedata between multiple CPUs of different controllers involved inoperating and controlling different machines. For example, the sharedmemory 320 may be any suitable memory and can allow the automationsystem 302 to provide instructions to be provided to the physicalmachine 370. The shared memory 320 can allow the instructions to beaccessed by multiple CPUs and can be used to store or access theinstructions being stored. In some instances, the instructions sets,e.g. NC program 353 may be accessed by the NC controller 314 and can beshared via related data sources, e.g., an HMI interface of thecorresponding physical machine 370. The instructions sets, e.g. NCprogram or NC code 353 is loaded onto the NC controller 314 and the NCcontroller 314 can execute each NC line in sequence. Each NC line numberexecuted (and, in some instances, the actual NC code, can be sent as anevent to the data collection system 202 as described herein, along witha particular counter value. In some instances, the machine managementmodule 311 and the data management module 323 may be a single componentor different components, or they may represent functionality performedby the automation system 302, where such functionality could beperformed by any number of components or elements.

In response to the execution of those instructions, data is returned tothe automation system 302 via the inbound signal interfaces 326, and theautomation system 302 can perform operations to enhance the data forfurther contextualization. Specifically, a high precision counter 344 isincluded in or associated with the automation system 302. Asillustrated, the high precision counter 344 is included within theautomation system 302—other implementations may place or include acounter 344 external to the automation system 302, such as where thecounter 344 is one of the data sources 380. The high precision counter344 may be a counter internal to the automation system 302 or it may bean input from an external data source 380. In general, the highprecision counter 344 provides a unique counter value that preciselyrelates the counter to the ingested data values and events identifiedand received at the automation system 302. As data is received via theinbound signal interface 326 and/or generated within the automationsystem 302 itself, a corresponding counter value is associated with thedata prior to further transmission or processing. Similarly, asinstructions are issued from the machine management module 311, anappropriate counter can be associated with the instructions to associatedata received by an inbound interface of the automation system 302 tocorrespond to instructions responsible for and/or triggering thespecific data. In some instances, the machine management module 311 maymanage the association of the counter value with particular data, whilein other instances any other suitable component may perform theassociation.

Memory 350 of the automation system 302 may represent a single memory ormultiple memories. The memory 350 may include any memory or databasemodule and may take the form of volatile or non-volatile memoryincluding, without limitation, magnetic media, optical media, randomaccess memory (RAM), read-only memory (ROM), removable media, or anyother suitable local or remote memory component. The memory 350 maystore various objects, data, or instructions (e.g., instructions sets,e.g. NC programs 353 and instructions, machine data 356 received duringthe session, sensor and/or source data 359 obtained during the session,and configuration data 360). Memory 350 may store other suitableinformation, including user information, administrative settings,password information, system information data and/or physical tool data,caches, applications, backup data, repositories storing business and/ormanufacturing process information, and any other appropriate informationassociated with the automation system 302 including any parameters,variables, algorithms, instructions, rules, constraints, or referencesthereto. Additionally, the memory 350 may store any other appropriatedata, such as VPN applications, firmware logs and policies, firewallpolicies, communication instructions, a security or access log, print orother reporting files, as well as others.

The machine data 356 may include data received from the physical machine370 during a particular session. In some instances, as the informationis received via the inbound signal interfaces 326, the information fromthe physical machine 370 can be stored in the machine data 356 andannotated or otherwise associated with the corresponding counter value.The machine data 356 can also store relatively static machineconfiguration and set up data, such as axis configuration and machineoffsets. In some instances, such information may be stored separately inthe configuration data 360. Similarly, sensor and/or source data 359received via the sensor signal interface 332 can be annotated and/orassociated with the corresponding counter value from when the data wasreceived. For instructions that are provided to the physical machine370, the instructions sets, e.g. NC program 353 may be annotated orotherwise associated with the corresponding counter value.

The outbound signal interfaces 335 are used to send real-time andnon-real time data to a data collection system 202 for furtherprocessing and contextualization. In some instances, the non-real timedata can be sent from the related data sources directly to the datacollection system 202 or the analytic system 130 without communicationvia the outbound signal interfaces 335. In such instances, the non-realtime data may be associated with the corresponding counter value whenthe data is generated/sent or at the point of being received by the datacollection system 202. As illustrated, a real time interface 338 and anon-real time interface 341 are provided, although additional channelsmay also be available. The real time interface 338 can be used totransmit raw information received at the automation system 302 to thedata collection system in deterministic real-time, such as where the rawinformation is capable of being used immediately in its raw form.Conversely, the non-real time interface 341 can be used, for example,where additional processing of raw data is required at the automationsystem 302 before such information can be provided to the datacollection system 302, or where alternate data not required for realtime motion control or real time process control is obtained but notused in real time. Real time data is sent to the data collection system202 and/or analytics system 130 from the automation system 302 (or, insome instances, directly from the physical machine 370 or sensors 375)regardless of whether data packets have been augmented with the highprecision counter at the automation system 302 or not. Processing thereal time and non-real time data occurs independent of the protocol usedfor communication. In other instances, the data can be enriched at otherlocations within the system 100. For any information transmitted viathese interfaces, the associated counter value is sent along with thedata or information to ensure that later contextualization can occur. Inthe illustrated example, non-real time information may be received witha certain delay through the outgoing non-real time interface 341,whereas real time information sent via the real time interface 338 isreceived in real time (deterministic, response within specified timeconstraint). In some instances, raw data received at the automationsystem 302 may be transmitted immediately, such as where nomodifications, calculations, or other actions need to be performedbefore the data may be usable or readable. In other instances, the rawdata received may need some processing, reformatting, or othermodifications before being sent to the data collection system 202. Inthose instances, data not being sent in real time can be sent via thenon-real time interface 341. Potential delays of the non-real time datareaching the next level of processing in the chain may be due to thedifference in bus access control mechanisms implemented in 802.3 and802.1Q. In one example, real time data is produced by cyclic executionof a data collection routine. For example, if a new line of instructionsstarts to be executed the counter value attached or related to the startof the new line of instructions is transmitted by the non-real timechannel as an event while the position data of the axis is transmittedvia a real-time channel. While the real-time channel is supported by thereal-time operation system and is transmitted immediately, the non-realtime channel data may be buffered first and transmitted eventually iftransmission or processing capacity of the non-real time operationsystem allows to process and transmit it. The leads into a situation insome instances in which the data which has a relation through thecounter values arrives at different points in time to the datacollection system 202. As noted, the counter value identifies the firstreceipt of the data at the automation system 302, and can ensure thateven where processing is required, such data can be placed at theappropriate location or in the appropriate order during furtherprocessing.

The term “real-time,” “real time,” “realtime,” or similar terms (asunderstood by one of ordinary skill in the art), means that an actionand a response need to be defined in a defined interval. The system hasto complete a job between release time and deadline, or else this systemwill be in an undefined state. The real-time-data is produced by cyclicexecution of a data collection routine as described above.

As illustrated, the automation system 302 includes interface 305 andprocessor 308. Interface 305 is used by the automation system 302 forcommunicating with other systems in a distributed environment—includingwithin the environment 100—connected to the automation system 302 and/ornetwork 190, e.g., physical machine(s) 370, sensors 375, and datasources 380, as well as data collection system 202, analytical system130, and client 105, as well as other systems communicably coupled tothe network 190. Generally, the interface 305 comprises logic encoded insoftware and/or hardware in a suitable combination and operable tocommunicate with the network 190 and other communicably coupledcomponents. More specifically, the interface 305 may comprise softwaresupporting one or more communication protocols associated withcommunications such that the automation system 302, network 190, and/orinterface's hardware is operable to communicate physical signals withinand outside of the illustrated environment 100.

Automation system 302 includes one or more processors 308. Althoughillustrated as multiple processors 308 in FIG. 3, a single processor maybe used according to particular needs, desires, or particularimplementations of the environment 100. Each processor 308 may be acentral processing unit (CPU), an application specific integratedcircuit (ASIC), a field-programmable gate array (FPGA), or anothersuitable component. Generally, the processor 308 executes instructionsand manipulates data to perform the operations of the automation system302, in particular those related to instructing and managing theoperation of the physical machine(s) 370, receiving incoming data, andtransmitting outgoing data to the data collection system 202.Specifically, the processor 308 executes at least a portion of thealgorithms and operations described in the illustrated figures asperformed by the automation system 302, as well as the various softwaremodules and functionality, including the functionality for sendingcommunications to and receiving transmissions from the automation system302 and to the data collection system 202, as well as to other devicesand systems. In particular, processor 308 can execute the machinemanagement module 311 and the data management module 323 as appropriate,as well as to operations of the physical machine(s) 370 and/or sensors375, where appropriate.

Regardless of the particular implementation, “software” includescomputer-readable instructions, firmware, wired and/or programmedhardware, or any combination thereof on a tangible medium (transitory ornon-transitory, as appropriate) operable when executed to perform atleast the processes and operations described herein. In fact, eachsoftware component may be fully or partially written or described in anyappropriate computer language including C, C++, JavaScript, Java™,Visual Basic, assembler, Perl®, any suitable version of 4GL, NumericalCode (NC), as well as others suitable to the devices or system uponwhich they are executed.

System 100 further includes the data collection system 202. The datacollection system 202 can perform operations associated withcontextualizing data as it is received from the automation system 302.In some instances, the data collection system 202 may be associated witha single automation system 302, while in other instances, the datacollection system 202 may be associated with a plurality of automationsystems 302 and their respective physical machines 370. The datacollection system 202 can execute a data collection, data processing andmanagement application 211 that acts to receive, process and manageincoming data sent from the real and non-real time channels of aparticular automation system 302 and can coordinate those sets of databased on an analysis of the data's value and/or the data's respectivecounter values as assigned at the automation system 302.

As illustrated, the data collection system 202 includes an interface205, a processor 208, a data collection and management application 211,and memory 223. The interface 205 and processor 208 may be similar to ordifferent from interface 305 and processor 308, respectively. Inparticular, interface 205 can manage communications and transmissionsreceived at and transmitted from the data collection system 202,including receiving data from the one or more automation systems 302 viathe real and non-real time feeds or channels established between thedata collection system 202 and the automation systems 302. Processor 208can execute the contextual cutting process managed by the datacollection, processing and management application 211, as well asperform other suitable functionality associated with the data collectionsystem 202.

The data collection and management application 211 is responsible forreceiving and managing the annotated or counter-associated data receivedfrom the automation systems 302. In particular, data is received throughvarious interfaces, including the real time interface 214 and thenon-real time interface 217. The application 211 can cause the real timedata to be stored in a real time data buffer 229 of memory 223. Data canbe stored there for a particular session of a particular physicalmachine 370 until an end event is received or a determination that anend-related or end-indicating signal is received at the data collectionsystem 202 for a particular session. The data may also be moved from thebuffer 229 in response to a determination that the buffer 229 is full.Other determinations and decision points as to how long data can bestored in the data buffer 229 may manage the storage time and buffer,and those times may be longer or shorter than the receipt ordetermination of an end event or signal. One such determination may bethe configuration of data collection system 202 for local, persistedstorage of data either in addition to sending chunks of data to theanalytics system 130, as well as the configuration of the analyticssystem 130 as known by the data collection system 202. Other factors maygovern or assist in the decision, as well. As non-real time data isreceived via the non-real time interface 217, the counter valueassociated with the non-real time data can be compared to the countervalues of the previously received real time data in the real time databuffer 229 by the contextual cutting module 220 or another process.Where the same counter value is identified, real time and non-real timedata can be contextualized to one another, and can be stored in thecontextualized data set 235. The process of contextualization cancontinue until an appropriate end event or signal defining the end of asession is received from the automation system 302 or another source,including one of the data sources 380. End events or signals may beexplicit based on a particular user input, a specific “stop” eventwithin the operations of the process, specific instructions in theinstruction code, e.g. NC code, indicating a stop or end event, resourcestarvation within the process, errors associated in the processtriggering a stop or end action, combined sets of data meeting an endevent condition, or a known stop process or value. Further, an end eventmay be triggered if a user indicates an end or stop to tracing theprocess. In response to the end event or signal, the contextual cuttingmodule 220 can format the contextualized data 235 into a finalized dataset and transmit the data set to the analytical system 130 foranalytical review and processing. The contextualized data set 235, orportions thereof, can be sent as a single data package upstream to theanalytical system 130 or it may be sent as separate data packagesdepending on size and system requirements or settings. In someinstances, a context starting event (e.g., an HMI-initiated startingaction, a first instruction code sentence triggered after a start event,an automated execution of a process, a known or pre-defined processentry or value), may be received from the automation system 302indicating that a start of a session has begun. Such contexts may causea new buffer 229 or package to be created, or to associate data receivedafter the starting indication with a new package. As additional data isreceived, it can be added to the data chunk as appropriate. If real timedata is received, it can be kept in the buffer 229 for a particularperiod of time, or until a non-real time piece of data or informationwith the same counter value is received. When matching non-real timedata is received, the matching real-time data can be added to the datapackage and removed from the buffer 229 and/or associated with thenon-real time data via metadata or other linking process. Based on theconnection of the counter values, the contextual cutting module 220 canassociate the data and prepare data for transmission to the analyticalsystem 130 for future processing.

Memory 223 may be similar to or different than memory 350, and includesthe real-time data buffer 229, any local copies of the non-real timedata 232, and the contextualized data 235. Memory 223 also includesconfiguration data, e.g., trace configuration 226 which determines whatdata is to be captured and considered relevant for future processing.Memory 223 can contain algorithms which for stand-alone localexecution/processing of the data or as part of distributed algorithmicexecution. Algorithms in memory are updated from analytic system 130when required. In some instances, significant amounts of data that arenot relevant or are not requested for monitoring can be excluded fromthe trace configuration 226 either statically or dynamically in responseto event from analytics-based monitor and control system 130 or theautomation system 302 and subsequent processing steps, such that datareceived from the automation system 302 may be deleted or not otherwiseprocessed using the contextual cutting module 220. The traceconfiguration 226 can allow process engineers or other suitable users102 to identify the data and information they view as important orrelevant to evaluating a particular process, thereby allowing the datacollection and management application 211 to simply ignore or delete thenon-relevant data prior to performing the matching process. The traceand processing configuration 226 may be managed by or otherwise definedat the analytical system 130, as well as provided as a defaultconfiguration upon installation and/or initialization of the systems.

FIG. 5 represents an example flow 500 for receiving real and non-realtime data as described herein and performing the contextual cutting ofthe received data. For clarity of presentation, the description thatfollows generally describes method 500 in the context of the system 100illustrated in FIGS. 1-3. However, it will be understood that method 500may be performed, for example, by any other suitable system,environment, software, and hardware, or a combination of systems,environments, software, and hardware as appropriate. In the presentillustration, the process is performed at the data collection system 202by the appropriate components and software.

Initially, it is noted that the data collection system 202 cancontinuously receive or obtain real-time and non-real time data from anautomation system 302 during processing and operations performed at aphysical machine or robot associated with the automation system. Inother words, the process described herein is related to an ongoingprocess that can be performed multiple times as the machine executes itsoperations.

In some instances, a start context event or indication may be initiallyreceived from the analytics-based monitor and control system 130 and/orthe automation system 302, indicating a new session or context is to becreated or initialized. If the data collection system 202 is associatedwith a single automation system 302 and a single physical machine 370,this start context event or indication may indicate that any priorsessions should be closed (e.g., as determined at 540 in response to anend event or condition) and/or any buffers storing real time data andnon-real time data may be cleared and transmitted, where appropriate. Ifno other sessions are currently being collected and contextualized, thefirst real time data received may be a first piece of data associatedwith a running session. In some instances, the first real time data maybe the first relevant piece of real time data to be associated with aprocess or meeting any specified condition. Where other sessionsassociated with another automation system 302 and/or another physicalmachine 370 are started, data associated with those sessions may bestarted in a concurrent processing thread and real time buffer separatefrom other systems.

At 502, tracing for real time and non-real time data associated with amanufacturing process may be initialized. The initialization of tracingfor a particular session may cause a data collection system 202 tosubscribe to one or more available feeds or streams of data associatedwith a manufacturing process. In some instances, the initialization oftracing may include an identification of particular information to besubscribed to for the manufacturing process. In some instances, that mayinclude all associated information, while in others, only a portion ofthe feeds or streams may be included. Further, the data collectionsystem 202 may subscribe to all feeds/streams but may ignore or discardsome data that does not match either the static tracing configurationassociated with the initialization or the dynamically providedcriteria/instructions from analytics-based monitor and control system130 or the automation system 302.

At 505, real time data is received at the system from the automationsystem (or alternatively, from the physical machine or sensorsassociated with the physical machine in some cases bypassing theautomation system), where each piece of data is associated with a uniquecounter value as generated by a high precision counter. As noted, thehigh precision counter may be a part of the automation system, or may bean incoming signal received at the automation system from an externalsystem or source. At 510, the received real time data is stored in astorage buffer, with each piece of stored data associated with itscorresponding counter value. In some instances, the data may beannotated or otherwise associated with the counter value. For example,if the buffer represents data stored in a database, the counter valuemay be stored in a field associated with the data in the database. Inother instances, the counter value may represent an annotation viametadata associated with the stored data. In still other instances, thecounter value may be concatenated onto the data prior to storage. Forexample, a set number of characters or a size of the saved data may bereserved for the counter value such that systems are easily able todetermine and identify the corresponding counter value. In short, anyother suitable manner of storing and associating the counter value withthe stored real time data may be used.

At 515, a determination is made as to whether non-real time data isreceived from the automation system and a start condition has beenevaluated. Alternatively, the start condition may be the driver for thesession and may contain real time data (which may be a source ofevaluation for the start condition). In general, non-real time data maybe received at any time during a particular session. However, if nostart condition has been met such that a session is being captured, thenfurther consideration of the non-real time data may be necessary. Aswith the real time data, the non-real time data will also be associatedwith a counter value as assigned from the high precision counter uponthe initial instance of the data being received at the automationsystem. If no non-real time data is received, then method 500 returns to505 where additional real time data may be received. It should be notedthat additional real time data may not be received before additionalnon-real time data is received, where method 500 returns to 515 for thereceipt of additional data. If, however, non-real time data associatedwith a particular counter value is received, method 500 continues at520.

At 520, in response to receiving the non-real time data, the stored realtime data is searched to identify whether any real time data isassociated with the counter value of the received non-real time data. At525, a determination is made as to whether a match is found. In someinstances, a match may be considered as found based on a relativenearness of counter values. For example, if an action is taken at timeN, but sensor data is received at time N+2 counter values, a match maystill be considered to be met. The relative threshold for non-exact“matches” may be built into the system as a default or may bedynamically determined based on potentially related or known connectionsof data within a predefined or dynamic time period. If no match isfound, then method 500 continues at 530, where the non-real time datacan be relatively ordered between any real time data with countersrespectively higher or lower than the received non-real time data, wherethe non-real time data can be stored in a data package being generatedfor the session or a part thereof. In such instances, the non-matchingnon-real time data can be ordered based on respective counter valuesbetween other non-real time and real time data in the packages providedto the data sink. If, however, a match is found, then the non-real timedata can be associated with the real time data having a matching countervalue at 535. In some instances, the non-real time data can be storedwithin the real time data buffer, while in others the data may beremoved from the buffer and stored as contextualized data outside of thereal data buffer. In still other instances, the non-real time data maybe stored separately from the real time data in the real time databuffer, where the stored non-real time data is connected to orassociated with the corresponding real time data based on its countervalue. The non-real time data may be associated with the real time datain a structured buffer or set of contextualized data, while in others,the non-real time data can be associated based on defined metadataassociated with the session. In any event, the non-real time data andthe real time data will be associated with the corresponding data setsin any suitable manner for further processing at the data sink. Whenpreparing chunks for sending to a data sink (e.g., the analytics system130), the data can easily be put together into a data package fortransmission as related sets of information.

At 537, as well as throughout the collection of data in the process, achunk of real time and non-real time data may optionally be generatedbased on related counter values and transmitted to the data sink beforean end event or condition is received or identified. In some instances,this may be based on the real time buffer being full or of a particularfullness percentage. In general, buffer management and maintenance maytake place throughout the collection process described herein, includingat the beginning of the process to clear out any existing data from thebuffer before data is collected for a session. During the process, thebuffer may be cleared and existing data packaged and sent if the bufferis full or if particular conditions are met (e.g., 10 real time eventsare received). If the buffer is filled while the process is ongoing, thechunk of session data included in the buffer and in the associated setof non-real time data can be moved to another location and/or system forstorage until an end event or condition is met, or can be packaged andtransmitted to the data sink for storage or immediate processing (oralternatively, the data can be stored in local storage at the datacollection system or elsewhere until a connection is made available,until a request for the chunk(s) is received, until an archive ortransmission condition is met to trigger transmission or storageelsewhere, or at any other suitable time based on system configurationand operations) until the completion of the session, when the remainingassociated data can be sent.

At 540, a determination is made as to whether the received events areassociated with an end event or end condition for the executing session.While illustrated as a determination of whether the non-real time datais associated with an end event, the real time data received at 505 mayalso be determined to constitute an end event or end condition. The endevent or condition may be a manual stopping of the process by a processengineer, human operator or any other suitable user interacting with thephysical machine or related data source, e.g. HMI panel, a remoteindication from another user authorized to stop the process, or an eventsignaling the completion of the instructions and operations associatedwith a session. Still further, the end event may not be explicit, butmay instead by a known set of instructions (e.g., a final instruction ina set of instructions being performed) or data/signals that indicatethat the session is completed or that an end event has occurred. Stillfurther, an end event can dynamically provided via criteria/instructionsfrom analytics-based monitor and control system or the automationsystem. If an end event or condition is not received or determined,method 500 can return to 505 to receive additional real time data and/or520 to receive additional non-real time data. While the determination ofthe end event or condition is illustrated after receipt of non-real timedata, the determination may be an ongoing determination performedthroughout the process, including based on the receipt of real time dataor combinations of real time and non-real time data or instructionsreceived from other components of the system, e.g. 130.

If, however, an end event or condition is determined to have beenreceived or is determined from the received data or instruction sets,method 500 can proceed to 545, where the context or session of datacollection is closed. At this time, at least a chunk of associatedand/or ordered real time and non-real time data is prepared for thecurrent activity and session based on the counter values of the data asreceived by the data collection system 202. The chunks may be sent as asingle set of data or individual chunks may be generated to manage thesize of data sent. In some instances, at least some of thecontextualized session data may already have been sent previously due toany number of factors, including a full or relatively full buffer,predetermined conditions associated with a clearing of the buffer or asending of information before the close of the session, near-real-timeanalytics in analytic system 130, or any other reason such as system 100configured for stream rather than set processing of session data. Basedon the counter values, the information is received and processed in thecorrect order, where related events occurring in response to one another(e.g., instruction causes particular action), in connection with oneanother (e.g., during an action, a particular sensor reading isgenerated), or in a particular order (e.g., real time or non-real timedata whose counter value does not match or correspond to another set ofdata) are associated and/or provided in the appropriate order forfurther processing. At 550, the chunks are transmitted to an associateddata sink for further storage and processing, such as the processesdescribed herein to further enrich, visualize, and interact with thereceived data. In some instances, the chunks may be provided to ananalytical system for further processing, although any system mayreceive the contextualized chunks for further action. In some instances,similar to 537, the chunks/data can be stored in local storage at thedata collection system or elsewhere until a connection is madeavailable, until a request for the chunk(s) is received, until anarchive or transmission condition is met to trigger transmission orstorage elsewhere, or at any other suitable time based on systemconfiguration and operations.

Returning to FIGS. 1-3 and system 100, the specific illustrations ofFIG. 1 are reviewed. In particular, the analytics-based monitor andcontrol system 130 and client 105 are presented. Upon completion ofparticular contextual cuttings of session data, the data collectionsystem 202 transmits those chunks or packages of data as described tothe analytics-based monitor and control system 130 for furtherprocessing, visualization, analytics presentations and control actions.Offline storage of data may also be provided as part of the datacollection system 202, where data and chunks from the contextualizationprocess can be optionally stored for periods of time prior totransmission to the analytics system 130 or elsewhere. In suchinstances, offline uploads of the data are available when connected,requested, or in response to a transmission request being received ortransmission condition being met. In some instances, the analyticssystem 130 may be associated with or be a part of the data collectionsystem 202.

In some instances, the analytics system 130 may be associated with acustomer system remote from a factory or manufacturing location wherethe data collection system 202, the automation system 302, and thephysical machines 370 may be located. In other implementations, theanalytics system 130 may be located at the manufacturing location alongwith the other systems, where appropriate. In any event, the analyticssystem 130 allows advanced visualizations, analytics, furthercontextualization to be performed on the data received at and initiallycontextualized by the data collection system 202 and control actions tobe performed on physical machine or subsystems thereof. The analyticssystem 130 may be a cloud-based system or may be associated with adedicated or on-premise system, where appropriate. In any event, theanalytics system 130 and its data are made available via network 190 toone or more clients 105 or other communicably connected systems, wherevisualizations and data may be displayed to allow for interactiveinvestigation and monitoring of the captured data. In some instances,the analytics system 130 may be a machine manufacturing analytics (MMA)system, where the MMA can evaluate the performance of the machine basedon the expected and observed data received from the machine and itscontroller/automation sy stem 302.

As illustrated, the analytics-based monitor and control system 130includes an interface 133, at least one processor 136, an analyticsapplication 139, and memory 166. The interface 133 allows theanalytics-based monitor and control system 130 to communicate withcommunicably coupled systems via network 190, and may be similar to ordifferent than interfaces 205 and 305 in operation for theanalytics-based monitor and control system. Similarly, processor 136 maybe similar to or different than processors 208, 308, and is used toexecute the instructions and functionality associated with the analyticssystem 130 as described herein. Memory 166 may be similar to ordifferent than memories 223, 350, and can store the contextualized setsof information received from the data collection system 202, which caninclude raw data 169, machine data 178, and NC program 175 (or anothertype of instructions executed by the physical machine 370), amongothers, as well as transformed data 172 based on the informationreceived from the data collection system 202, where the transformed data172 is based on one or more kinematic transformations performed upon thereceived data, such as to generate a data set that provides a commonunderlying coordinate system. While illustrated inside the analyticssystem 130, memory 166 may reside outside of the analytics system 130 orbe remotely location from the analytics system 130 in otherimplementations, and may be a data lake or storage cluster, amongothers. Data received from various inputs (e.g., the physical machine370 itself, various sensors 375 and data sources 380) and the dataprovided or associated with the instructions sets, e.g. NC program code353 and its commanded or expected values, as well as other dataelements, may be presented in two or more different coordinate systems.Positional and spatial information may be represented in a multitude ofcoordinate systems, ranging from local, relative systems, to globalcoordinate systems. Examples would include axis positional informationthat would be in an axis coordinate system, typically based on eitherlinear or rotary coordinate system. Machine positional information maybe reflected in various machine coordinate systems in 3D space, whichwould be based on Cartesian Coordinate system. Workpiece designinformation may be expressed in a Design Coordinate System. In additionto the Design Coordinate System, various Workpiece Coordinate Systemsmay be defined, where the reference is a known and measurable pointlocated on the workpiece. While any of the general coordinate systemscan be used as a singular reference, a single Workpiece CoordinateSystem is typically chosen as the common coordinate system, and as such,values expressed in the various coordinate systems are transformed intoa common coordinate system. To allow for presentation of the actual andexpected values in a 3D visualization, the analytics system 130 mustperform kinematic transformations of the received data to the commoncoordinate system in order to make available usable data for easycomparison, visualization, analytics-based monitoring and control.

The analytics application 139 may be a single application comprisingmultiple functionalities or may be a combined set of multipleapplications, software, agents, or other functionality, as appropriate,including a distributed application between code executed on analyticssystem 130 and/or data collection system 202 and/or physical machine370. In general, the analytics application 139 stores and transformsdata received from the data collection system 202, associates andinteracts with such data as it relates to particular sessions of themanufacturing or other machine process, presents the raw and transformeddata into one or more visualizations to users (e.g., process engineer orother suitable users 102) to review differentials between actual andexpected actions performed by the machines 370, and to identifyparticular inputs and data to be captured for future processing and/orvisualization, among others. Alternatively, the application 139communicates analytic results, e.g. differentials between actual orexpected performance, predicted deviations to control application toinitiate corrective actions by communicating events/commands to physicalmachine or subsystems.

As illustrated, the analytics application 139 includes an analyticsprocedure module 142, which as illustrated comprises a critical regionmodule 145, a contouring error module 148, and a kinematictransformation module 151 (although the analytics procedure module 142may contain other analytics procedure modules in other implementations),a session service 154, an offline session management service 157, atrace configuration module 160, and an online trace runtime manager 163.

The analytics procedure module 142 can perform various operations ingenerating and interacting with the data obtained from the datacollection system 202. For example, the kinematic transformation module151 can perform operations to transform both actual and expected datainto a common coordinate system, which in some cases may be aprerequisite for interpreting and interacting with the data received atthe analytics system 130 and describing the actual actions of thephysical machine 370 while performing the set of instructions thatdescribe an expected set of actions. The contouring error module 148 ofthe analytics procedure modules 142 can operate on the data after thetransformation into the common coordinate system, where the contouringerror module 148 calculates deviations from the actual path of a tool ofthe physical machine 370 as compared to the expected or commanded pathas defined in the underlying instruction set (e.g., the NC program 175).The contouring error can be represented as a percentage of differencebetween the expected path and the actual path. Some contouring errorsmay exist based on extremely small impacts or imperfections in thephysical machine 370, as well as based on vibrations and/or othermovements at the machine or machine 370. In some instances, particularsizes of deviations may exceed a particular level and/or desired amount,which may lead to workpiece quality issues. In those instances, acritical region may be identified, such as by the critical region module145. The critical region module 145 may identify particular thresholdsfor various metrics, including the contouring error, where criticalregions may exist. When metrics and/or raw data violate the particularthreshold, the critical region module 145 can identify those values asbeing a critical region. The particular critical region thresholds maybe user-configured or defined, default values based on general deviationpercentages or amounts, or they may automatically be determined based ona statistical review (e.g., more than a standard deviation variation,more than two standard deviations, etc.). The critical region module 145can identify one or more particular critical regions within the data setand identify them as issues during or in the context of a particularpresentation, visualization, and/or analytical review.

FIG. 6 illustrates an example process for transforming the data of bothexpected and actual data sets into the common coordinate system, as wellas determinations of potential critical regions based on contouringerrors and other potential issues. In particular, FIG. 6 represents anexample flow 600 for transforming received expected and actual datavalues from the coordinate systems in which they are received into acommon coordinate system, allowing those actual and expected actions tobe compared in a particular visualization based on a common underlyingcoordinate system. Any suitable coordinate systems may be used. In somecases, at least some of the data may already be in the appropriatecoordinate system upon receipt such that no further transformation maybe needed. For clarity of presentation, the description that followsgenerally describes method 600 in the context of the system 100illustrated in FIGS. 1-3. However, it will be understood that method 600may be performed, for example, by any other suitable system,environment, software, and hardware, or a combination of systems,environments, software, and hardware as appropriate. In the presentillustration, the process is performed at the analytics system 130 bythe appropriate components and software, although other such systems orcomponents may perform the operations in other implementations.

At 605, the chunks of associated data associated with the manufacturingprocess can be accessed for further transformation. In some instances ofthe system 100, method 600 may begin immediately or near-immediatelyupon receipt of the data from the data collection system 202 such thatthe transformed data is available in a real-time or near-real-timemanner.

As illustrated, method 600 illustrates a split processing of theexpected values 610 and the actual values 615, although the processingmay be performed in a combined or similar manner across the actual andexpected values. Further, different portions of the actual values 615,and in some cases, the expected values 610, may be associated withdifferent coordinate systems even among their own type (e.g., two sourceof actual data with different coordinate systems) of data. Therefore,the actions of 620, 625 and 630, 635 may be performed concurrently orsequentially for various different portions of the actual and/orexpected data.

At 620, the coordinate system of the expected values 610 is identified.The identification may be performed by an analysis of the received data,pre-existing knowledge of the coordinate system used by the expecteddata, or other information or metadata associated with the receivedexpected data, or by user selection or configuration. In some instances,information about the set of instructions associated with the expecteddata may be analyzed or known and can be used to identify the particularcoordinate system of the expected values. Upon identifying the receivedcoordinate system, method 600 continues at 625 where the data in theidentified coordinate system is translated to a common coordinate systemused by the analytics system 130. In some instances, the commoncoordinate system may be a machine coordinate system or other suitablecoordinate system. In some instances, the translation of the data to thecommon coordinate system may be a relatively simple shift of the data bya constant value in one or more planes. In other instances, thetransformation may be a significant calculation and translation, wheremultiple mathematical kinematic transformations are required to placethe received data into the common coordinate system.

The actual values 615 can have the similar operations 630, 635 performedupon them, where the particular coordinate systems of the receivedactual values 615 are identified at 630 and translated into the commoncoordinate system at 635. As noted, different portions of the actualvalues 615 may be in different coordinate systems such that each set ofdata within a session or chunk of a session may need to be processeddifferently.

While the illustrated method 600 includes the transformation of bothexpected and actual values into a common coordinate system, in someinstances, expected or commanded values may not be available. In suchinstances, operations 610-625 will not be performed. However, each setof actual data can be transformed into the common coordinate system inorder to allow a comparison of actual data received from varioussources.

Once the transformations of the data are complete, method 600 continuesto 640. At 640, the commanded data and actual data in the chunk orsession are correlated based on the spatial context of the respectivedata sets. For example, mathematical connections are created between theactual and expected data based on where paths of the actual path takenby a tool or end-effector of the physical machine 370 went and where theexpected values were expected to cause the path to go. In manyinstances, an exact match may not be available such that a detailedanalysis of the actual and expected paths may be performed andparticular actions or movements within the 3D area of the physicalmachine's tool path may be connected based on their relative spatialcontexts. The expected values may be precise while the actual values andthe paths they represent may be irregular based on vibrations or otherphenomena occurring with the physical machine 370, unexpected movementsduring the actual operations, and other non-expected actions or results.Further, the timing of the actual data will differ from the timing ofthe expected data, as delays in implementing particular instructions mayoccur or be influenced by outside factors. In many instances, the set ofinstructions associated with the expected values may not specific aparticular velocity of an action or may not require a particular timingof an action. If actual and expected values were associated based ontiming alone, even a simple offset, the connections between the valuesmay be off or incorrect. Therefore, correlating based on the spatialaspects of the path traveled allow for an accurate connection for futureanalytics. While the correlation is not based on a particular time theaction occurred, at 645 the spatially-correlated data can becontextualized further with a time-based reference. The time-basedreference can act as a connection or anchor for future visualizationsand interactions, and may be assigned a time the actual data isassociated with or the time the expected data is associated with. Inmany instances, the time the actual data occurred may be a more helpfulanchor, as users can review the timing of the actual execution of theoperations instead of the time of the expected operation (e.g., when aparticular instruction set was provided). In some instances, thecontextualization may be associated with the basis of the expected data,that is the instruction set (e.g., NC program instructions or code) thatdefines what the expected actions are when executed. In such instances,the set of instructions can be associated with the corresponding timereference, thereby allowing further connections amongst the expected andactual data values for future reference and comparison.

At 650, a comparison of the correlated data sets can be automaticallyperformed, if both actual and expected values are available.Alternatively, or in addition, one or more critical regions can bedetermined at 650, where critical region detection may be based on acomparison of expected vs actual values, but can also beexecuted/performed on other data sets including only actual values(e.g., actual force or vibration data). For example, a contouring erroracross the paths taken and expected can be determined, and, in someinstances, one or more critical regions may be identified based on thecontouring error. In some instances, the comparison may identifyparticular time or location references associated with the correlatedsets of data such that, when a presentation of any metric orvisualization associated with the session is viewed, that one or moreindicators of particular critical regions are illustrated. For example,particular points or portions of a path traveled illustrated within a 3Dcanvas may be highlighted, emphasized, or otherwise distinguished toindicate where a critical region exists. Similarly, if a presentation ofparticular instructions sets is provided in a window associated with thepresentation, one or more instructions associated with a critical regioncan be highlighted or shown with a distinguishing feature, such as acolor-coded presentation. Still further, if a 2D metric is presentedthen any determined critical regions may be presented in a differentcolor, highlighted or shaded, or otherwise distinguished from theportions of the metric that are not associated with a determinedcritical region. As users may be able to adjust the definition and/orthresholds associated with critical regions, what is considered acritical region may dynamically update in response to user modificationsto the threshold values. Additionally, dynamic thresholds may bedetermined based on updated models, either within a particularmanufacturing process or across different manufacturing processes. Inother words, thresholds for critical region detection may be user- orsystem-defined, and may be static or dynamic values/amounts.

At 655, an interactive 3D visualization associated with the actualand/or expected data can be presented. In some instances, informationassociated with the comparison of 650 can be presented within userinterfaces.

Returning to FIGS. 1-3, the analytics application 139 also includes asession service 154. The session service 154 may be an applicationprogramming interface (API), a set of APIs, or other modules or agentsof or associated with the analytics application 139 that allow users(e.g., process engineers or other suitable users 102) to view historicaland ongoing sessions associated with one or more manufacturing processesthat are or have been monitored, captured and controlled by the system100. The session service 154 can interact with at least some of the data(e.g., raw data 169, transformed data 172, NC program code 175, machinedata 178, and workpiece design data 181, among others) to provideinformation about particular individual sessions, groups of sessions,and/or portions of sessions. In some instances, the session service 154may provide information about a currently executing session from whichdata is being collected and prepared. In others, the session service 154may provide access to previously performed sessions stored in memory166. In general, a session may be defined as a recorded trace with adefined start and end associated with a particular manufacturing processperformed by one or more physical machines, e.g. machine tools, robotsand related tools/end-effectors from which data is collected and madeavailable. While not shown in memory 166, some or all of the stored datamay be separated in or specifically associated with particular sessionsexecuted and monitored by the illustrated systems. To access and presentdata, particular sessions can be identified and the associated dataloaded for interaction and visualization.

The offline session management service 157 may be an API, a set of APIs,or other modules or agents of or associated with the analyticsapplication 139 that handle ingestion of session data in an offline mode(e.g., via file or archive loading). In some instances, the offlinesession management service 157 may include a GUI within the analyticsapplication 139 that allows users to load archived historical data.

The analytics application 139 includes a trace configuration module 160.The trace configuration module 160 allows users or system componentssuch as control application to define particular trace configurations184 to be pushed down to the data collection system 202 (i.e., traceconfiguration 226). A trace configuration 184 identifies particularparameters, sensor input, metrics, and other information to be includedin a set of session data. Based on the trace configuration 184, the datacollection system 202 can, in some instances, filter out input orotherwise process, e.g. aggregate, compress, based on traceconfiguration 184 received via the various interfaces 214, 217, therebyremoving or reducing the non-required data from the data set. The traceconfiguration 184 can also include definitions of start and endconditions that would be used by the data collection system 202 forcutting contextualization. When those conditions are met, the cuttingprocess can react accordingly. In some instances, the traceconfiguration 184 may be pushed down to the automation system 302 and/orparticular machines 370, sensors 375, and data sources 380 such thatnon-required data either is not generated or not passed upward tothrough the system 100. The trace configuration module 160 allows usersto define the particular parameters, metrics, and other data to becaptured and included. In some instances, if no particular modificationsare defined, the trace configuration 184 may define a default set ofdata and metrics to collect. Should additional or less information beneeded, the user can interact with the trace configuration module 160 tomodify the desired data set, with changes being passed to the variouscomponents managing the collection and/or processing of relevant data.

The online trace runtime manager 163 can manage the receipt, ingestion,and processing of data from the data collection system 202. In someinstances, the online trace runtime manager 163 may be associated withone or more REST-based APIs to which information from the datacollection system 202 can send session information. As data is received,the online trace runtime manager 163 can receive and store the data inreal time or as it is received. If some instances where the data isreceived in a piecemeal fashion, the online trace runtime manager 163can store the data in a buffer before receiving the end of the session,where the data can then be stored. Alternatively, the data can be storedby the online trace runtime manager 163 as it arrives, with sessionsbeing closed upon receipt of the end event data. In some instances, theonline trace runtime manager 163 can perform filtering out of dataoutside the trace configuration 184 where no other filtering has beenperformed within the system (e.g., where the data collection system 202does not act upon a particular trace configuration 226 and merelycollects and contextualizes all data received from the automationsystems 302.

FIG. 4 illustrates three tracing examples—a first scenario where notracing is performed (405), a second where a first set of variables andparameters are traced based on a user's selection (410), and a thirdwhere an updated set of variables and parameters are selected based onan updated selection (415). In scenario 405, a manufacturing process isperformed without tracing. A local operator of the physical machine 370can initiate the process by loading a particular instruction set, e.g.NC program (e.g., selecting an existing program that has been previouslyloaded into the memory of the machine, or by loading and activating anew program either via USB storage media, or other method.). The NCprogram may be copied at this time to the data collection system 202prior to the start of manufacturing, or may be copied at the start,during, or end of manufacturing. The operator would initiate the machineprocess (e.g. by pressing the “Start” button on machine interface,causing the manufacturing process to initiate. The workpiece is thenmanufactured, but with no data being traced. Upon completion, no dataassociated with the session is traced or recorded.

In scenario 410, a process engineer, via the analytics system 130, canidentify a set of desired variables and parameters to be traced (i.e., aparticular trace configuration) and can enable the tracing via the traceconfiguration module 160. The analytics system 130 can provide the traceconfiguration to the data collection system 202 which activates thetracing process and begins capturing data as the manufacturing processis initialized again by the local operator. As the system executes,trace data is received by the data collection system 202 and is managedand contextualized based on the counter value associated with theparticular real time and non-real time data. As the process begins, thedata collection system 202 can provide information regarding the newsession to the analytics system 130 in preparation for capturing andtransformation of the data. A new session is created by the analyticssystem 130, and as information is received from the data collectionsystem 202, the data is transformed and associated based on the data'sspatial characteristics. Additional data is associated with the captureddata and, upon completion of the process, the session is completed. Theprocess engineer or another user is then able to access and interactwith the data from the completed session.

In scenario 415, additional or alternative variables can be identifiedby the process engineer and subsequently passed down to the datacollection system 202 via the updated trace configuration. The datacollection system 202 updates what is relevant to the current process,and can perform a similar process as scenario 410 for creating a newsession at the analytics system 130 and associating traced data based onthe counter values. Again, upon completion of the session, the sessioncan be completed and the user can view the completed session.

At 420, the process engineer may determine no further tracing is needed.

In response to the indication, the analytics system 130 can notify thedata collection system 202 to disable further tracing. If futureprocesses are initiated, the data collection system 202 can ignore thedata similar to scenario 405.

The illustrated flows of FIG. 4 are merely one example set of operationsfor the tracing and manufacturing processes. In addition to human actorsinitiating the illustrated flows, these flows can also be initiated bycomponents of the system, e.g. agents, workflow engines. For example,one instance not depicted is the user accessing the session while it isin progress, as well as making changes to the trace configuration whilea particular manufacturing process is executed. Other suchimplementations are possible and contemplated by the present disclosure.

The analytics system 130 further includes a set of visualization and UIlogic 164 that allows users to view and interact with the various setsof session data. The set of visualization and UI logic 164 can providethe functionality associated with several of the solutions previouslymentioned and later described in this disclosure, in particular the 3Dvisualization of the session data, the presentation of 2D metric dataintegrated within the 3D visualization of the session data, the four-wayinteraction of related data presentations, and pattern-basedbenchmarking. The visualization and UI logic 164 is capable of accessingparticular sessions or groups of sessions and visualizing thecorresponding session data to users.

In an example 3D visualization, the visualization and UI logic 164 canaccess a set of session data and present, e.g., in an animatedpresentation, a visualization of an actual path taken and/or expectedpath expected to be taken by a tool of the physical machine 370. Thevisualization may present the entirety of the path taken or expected tobe taken based on the transformed data 172 generated at the analyticsapplication 139 by the kinematic transformation module 151. If both theactual and expected data related to the path is shown, different colorsmay be used to differentiate between the two paths. In some instances, aCAD-based image of the particular workpiece being manufactured (e.g.,milled, drilled, machined, welded, etc.) may be presented along with thevisualized path or paths. To do so, the actual and expected paths anddata can be transformed into a common coordinate system, and the CADimage can be transformed as well into the same common coordinate system.In some instances, the coordinate system of the CAD image may be thesame as the common coordinate system such that no transformation isrequired for the CAD image data. The visualized paths can show theactions taken or expected to be taken by the machine 370 as the sessionoperated. Based on particular calculations (e.g., the contouring errorcalculation) and thresholds specified (either automatically determinedor user-defined), particular portions of the paths taken can beidentified as critical regions where the calculated values exceed thosethresholds resulting in a critical region being determined. In someinstances, raw data or metrics may be used to identify critical regions.For example, where a vibration amount or temperature level is exceededbased on sensor data, a critical region may be determined. Based on thecontextual link to the actual and/or expected path data, the metricvalues identifying the critical region can be used to determine portionsof the paths taken and/or the associated sets of instructions (e.g.,particular lines in the NC program 175) where the critical region isassociated, which can then be shown within the 3D visualization.

The visualization and UI logic 164 for the presentation of 2D metricdata integrated within the 3D visualization of the session data, thefour-way interaction of related data presentations, and thepattern-based benchmarking can provide the additional connections andfunctionality described in FIGS. 7, 8, and 9, along with the otherexample illustrations.

The analytics-based monitor and control system 130 may be a cloud-basedsystem, a local server at a customer location, a remotely accessibleserver or system, or any other appropriate system. In any event, theanalytics system 130 and its functionality can be accessible to one ormore clients 105 communicably coupled to the analytics system 130. Insome instances, the client 105 may be associated with one or moreprocess engineers or other suitable users 102, as well as any othersuitable users or administrators.

As illustrated, one or more clients 105 may be present in the examplesystem 100. Each client 105 may interact with the analytics-basedmonitor and control system 130 via network 190, such as through the MMAapplication 114 or any other suitable client application. Multipleclients 105 may interact with and access the functionality of theanalytics system 130. In some instances, the client 105 may accesssimilar sessions associated with a particular machine 370 and itsrelated sensors, while in others different clients 105 can access datarelated to sessions of different machines 370 at the same time. Eachclient 105 may include an interface 108 for communication (similar to ordifferent from interfaces 133, 205, 305), a processor 111 (similar to ordifferent from processors 136, 208, 308), the MMA (or client)application 114, memory 120 (similar to or different from memory 166,223, and 350), and a graphical user interface (GUI) 117.

The illustrated client 105 is intended to encompass any computing devicesuch as a desktop computer, laptop/notebook computer, mobile device,smartphone, personal data assistant (PDA), tablet computing device, oneor more processors within these devices, or any other suitableprocessing device. In general, the client 105 and its components may beadapted to execute any operating system, including Linux, UNIX, Windows,Mac OS®, Java™, Android™, or iOS. In some instances, the client 105 maycomprise a computer that includes an input device, such as a keypad,touch screen, or other device(s) that can interact with the MMAapplication 114, and an output device that conveys informationassociated with the operation of the applications and their applicationwindows to the user of the client 105. Such information may includedigital data, visual information, or a GUI 117, as shown with respect tothe client 105. Specifically, the client 105 may be any computing deviceoperable to communicate queries or requests for information regardingparticular sessions stored by and available at the analytics system 130,as well as communicate in some instances with other components withinand outside system 100, e.g., via network 190, as well as with thenetwork 190 itself, using a wireline or wireless connection. In general,client 105 comprises an electronic computer device operable to receive,transmit, process, and store any appropriate data associated with theenvironment 100 of FIGS. 1-3.

The MMA application 114 may be any suitable application executable onthe client 105, including a web-based application or local clientapplication. The MMA application 114 may be an agent of the analyticssystem 130 or may access information stored and managed by the analyticssystem 130 to interact with the stored session data. GUI 117 of theclient 105 interfaces with at least a portion of the environment 100 forany suitable purpose, including generating a visual representation ofthe MMA application 114 and the visualizations and interactions with thesession data associated with one or more manufacturing processes andphysical machines 370. In particular, the GUI 117 may be used to presentvisual representations of the session data including illustrations ofthe actual and/or expected paths taken by a particular physical machine370 or robot, illustrations of a particular workpiece being manufacturedor worked on during the session, metrics and data measured during orcalculated from the actions of the particular sessions, information onparticular sets of instructions provided to and executed by the physicalmachines 370 or other machines during the session, as well as otherrelevant information. The GUI 117 may be used to visually presentvarious analytical analyses and provide interactive manipulations ofdata from the sessions using the functionality of the MMA application114. GUI 117 may also be used to view and interact with various Webpages, applications, and Web services located local or external to theclient 105. Generally, the GUI 117 provides the user with an efficientand user-friendly presentation of data provided by or communicatedwithin the system. The GUI 117 may comprise a plurality of customizableframes or views having interactive fields, pull-down lists, and buttonsoperated by the user or process engineer or other suitable users 102.For example, the GUI 117 may provide the interactive visualizationsdescribed herein that allow a user to view or interact with session datarelated to the operations and execution of manufacturing processesassociated with the system 100. In general, the GUI 117 is oftenconfigurable, supports a combination of tables and graphs (bar, line,pie, status dials, etc.), and is able to build real-time portals,application windows, and presentations. Therefore, the GUI 117contemplates any suitable graphical user interface, such as acombination of a generic web browser, a web-enable application,intelligent engine, and command line interface (CLI) that processesinformation in the platform and efficiently presents the results to theuser visually.

While portions of the elements illustrated in FIGS. 1-3 are shown asindividual modules that implement the various features and functionalitythrough various objects, methods, or other processes, the software mayinstead include a number of sub-modules, third-party services,components, libraries, and such, as appropriate. Conversely, thefeatures and functionality of various components can be combined intosingle components as appropriate.

FIG. 7 illustrates an example process for displaying two-dimensional(2D) metric data associated with a three-dimensional (3D) visualizationof a manufacturing process session onto the 3D visualization. Forclarity of presentation, the description that follows generallydescribes method 700 in the context of the system 100 illustrated inFIGS. 1-3. However, it will be understood that method 700 may beperformed, for example, by any other suitable system, environment,software, and hardware, or a combination of systems, environments,software, and hardware as appropriate.

As described previously, one or more two-dimensional (2D) sets of dataassociated with the various calculations and/or measurements may beavailable to provide additional information, context, and issuerecognition capabilities to the user. In previous solutions, suchpresentations were provided separately from a 3D visualization of amachine's tool path traveled during the manufacturing process session.In other words, a first window may present a 3D visualization of themachine's tool path during the process, while another separate windowmay provide information on one or more metrics measured during theprocess. The metrics may be calculated metrics, such as a contouringerror determined based on a percentage difference from an actual pathtaken by the machine and an expected path on which the machine wasinstructed to take, as well as parameters or other sensor-based dataidentified during the process (e.g., temperature, electrical pulses,vibration, velocity, jitter, or any other suitable signal or parameter).Manual comparisons and analysis comparing the 3D visualization and the2D metrics are time consuming. The described solution here thereforeallows 2D metric data to be rendered on the 3D visualization within thesame window and presentation of machine-related data. In providing theconnection, the portions of a path taken by the machine can beidentified in connection with areas where potential critical areas aredetermined based on the 2D metrics. Distinguishing or highlightedpresentations of the 2D metric values and the areas along the pathtraveled within the 3D visualization window or presentation area canallow for immediate recognition of impacted area. In FIG. 7, an exampleprocess for presenting 2D metrics within and in association with the 3Dvisualization are provided.

At 705, a 3D visualization of machine-related data obtained from amanufacturing process session for a particular workpiece is presented.The 3D visualization is associated with a traveled tool path during themanufacturing process, where the tool path is traversed by the machineoperating in 3D space for manufacturing the workpiece. In someinstances, the path of the tool visualized is the actual path of thetool traveled (or a particular component or part of the machine) duringthe session, while in others the path of the tool visualized is anexpected path to be traveled by the tool during the manufacturingsession. The tool path can be determined based on the expected valuesfrom the controller, which are determined by the controller based on theset of instructions, e.g. NC code provided to the machine. In someinstances, the tool path may be purely derived from the instructionsthemselves, or they may be determined by the controller after providingthe instructions to the machine.

At 710, at least one 2D metric data set associated with themanufacturing process session can be presented within the same userinterface as the 3D visualization. Where the 3D visualization ispresented in a first presentation area or window of the user interface,the 2D metric data sets are presented in a second presentation area orwindow of the UI separate from the first presentation area or window ofthe user interface. The 2D metric data sets may be metric valuesevaluated over a period of time occurring during the manufacturingprocess session. In some instances, the 2D metric data sets may beobserved or calculated data as obtained by one or more sensors of orassociated with the machine, while in others, the 2D metric data setsmay be calculated values based on the actual path traveled by themachine during the session, the expected path to be traveled by themachine during the session, or a comparison between the actual pathtraveled by the machine and the expected path to be traveled by themachine during the session (e.g., the contouring error). Any suitablemetric can be presented associated with the manufacturing processsession, the machine, or sensors and other data associated with theprocess. In some instances, the 2D metric data sets may be presentedprior to the presentation of the 3D visualization, such as when 2Dmetric data sets for a plurality of different manufacturing sessions areavailable. In such instances, a corresponding visualization may bepresented only after a particular 2D metric data set is selected.

At 715, a selection of a particular 2D metric data set is received,where the selection is an indication that the selected 2D metric dataset is requested to be presented in connection with the presented 3Dvisualization.

At 720, a connection between values of the selected 2D metric set andthe machine-related data associated with the 3D visualization isdetermined. In some instances, the machine-related data associated withthe 3D visualization may be associated with time references for pointsalong the path traveled by the machine. In other instances, themachine-related data associated with the 3D visualization may beassociated with particular distances traveled by the machine or aportion of the machine during the manufacturing process session. Inother instances, a spatial location associated with the both the 2Dmetric set and the data of the 3D visualization may be used to connectthe information. Similarly, the 2D metric set is a two-dimensional dataset associated with a metric value and a time, a distance traveled, oranother similar second dimension. The connection between the 2D metricset and the machine-related data can be determined based on a connectionbetween the times of the values of the metric set and a time at whichthe machine was at a particular location, or between a distance traveledby the machine in the machine-related data and a distance traveledwithin the 2D metric set. In instances where the 2D metric represents acalculation derived from the machine-related data, the connection isbased on the comparison between the known similar timing ordistance-traveled values of the two sets of data.

Once the connection is determined, the 2D metric set is incorporatedinto the 3D visualization of the machine-related data based on thedetermined connection between the two data sets. Based on theconnection, the values of the 2D metric set can be presented in a 2Dformat on top of the 3D visualization based on the points in common,based on the time of the 3D visualization matching the 2D value, orbased on a common distance traveled for part of the 3D visualizationmatching the time traveled for corresponding to a particular 2D value inthe 2D metric set.

In some instances, a CAD-based visualization of the workpiece beingmanufactured or otherwise worked on by the manufacturing process may beincorporated into the 3D visualization, either before or after theselection of the particular 2D metric. The CAD visualization of theworkpiece may be presented at a particular rotation or angle. In someinstances, the angle of presentation of the 2D metric set in the 3Dvisualization may be based on an angle or orientation of presentation ofthe CAD visualization of the workpiece. In some instances, the angle ororientation of the 2D metric set may match the angle or orientation ofthe tool or element of the machine as reference. The angle ororientation may be varying and different throughout the 2D metric set.Further, the presentation of the 2D metric set may, after beingincorporated to the 3D visualization, rotate in connection with anyrotation of the workpiece.

In some instances, particularly where the values of the 2D metric setvary widely, the size of the values from the 2D metric set may bepresented in a reduced scale to better fit within the 3D visualization.The reduced scale may be a uniform reduction to every value of the 2Dmetric set, while in others a logarithmic or non-uniform suitablescaling used to present the relative values of the metric at differenttimes without requiring an exact scale.

In some instances, only a portion of a 2D metric set may be presented inconnection with the 3D visualization. In some instances, the 2D metricset may have one or more ranges of values falling in a critical regionbased on a pre-determined, default, or dynamically-determined thresholdvalue indicating a potential issue with the manufacturing process. Insome instances, incorporating the 2D metric set into the 3Dvisualization may include only incorporating portions of the 2D metricset associated with a critical region. In some instances, users maytoggle whether to present the entirety of the 2D metric set or a portionthereof, as well as to define particular time ranges or distancestraveled in which the 2D metric is to be shown.

Depending on the type of 2D metric, positive and negative values may bepossible. Negative values can be presented in any suitable manner,including drawn opposite the positive angle or orientation. In otherinstances, negative values can be drawn at the same angle or orientationas positive values but with a differentiator indicating the negativeaspect of the value, including a different color or shading.

FIG. 8 illustrates an example process for providing a multiple window orpresentation area presentation of different views on a common underlyingset of data associated with a manufacturing process session. For clarityof presentation, the description that follows generally describes method800 in the context of the system 100 illustrated in FIGS. 1-3. However,it will be understood that method 800 may be performed, for example, byany other suitable system, environment, software, and hardware, or acombination of systems, environments, software, and hardware asappropriate.

In some instances, the solution described in relation to FIG. 8 iscalled a four-way interaction based on a common implementation havingfour different windows. However, any number of windows or presentationareas may be used, as appropriate. References to the solution as“four-way interaction” are not meant to be limiting, but describe aparticular example implementation. Specifically, the interactiondescribed may be an n-way interaction, where n is an integer of 2 ormore. The objective of four-way interaction is to help users navigatemassive amounts of data that are otherwise impossible to analyze, byhelping them narrow down to a specific problem area across presentationsof different views on the common underlying data set. The solutionprovides four different interconnected user interface controls that areavailable to view and analyze different parameters and navigate portionsof time and distance traveled by the machine to get insights from themachine-related data which would otherwise have to be manually pulledfrom historical data.

Specifically, the four-way interaction is enabled through thepresentation of separate, but connected, selectable presentation areas.In one implementation, the solution may provide the four presentationareas comprising a time slider, an NC code (or, more generally, a set ofinstructions executed by the machine) selection box, an interactive 3Dvisualization illustrating the path taken by the tool during themanufacturing process session, and a 2D metric chart region providingcalculations and other detected or calculated parameters over time.

The data presented in each of the presentation areas is related toand/or connected to one other and the underlying data set of themachine-related data, where each point of the underlying data set islinked based on inherent or external data connections. For example, thepath traveled by the tool of the machine may be associated with locationinformation, a distance traveled by the tool, and a time at which themachine was at different positions. Similarly, the set of instructionsexecuted by the machine may be associated with a reference value, suchas a time at which a particular instruction was executed. The 2D metricsfor various parameters and metrics may be associated with a distancetraveled, a time, or another reference value, such as a spatialreference. In some instances, the reference values may not be explicitlydefined within the data, but instead defined in metadata associated withthe particular data. An example may be the set of instructions, wherethe instructions themselves are not explicitly associated with a time ofexecution. Instead, a set of metadata may be generated during the dataingestion process to associated a particular time of executionassociated with different instructions or lines of instruction. Based ontheir connection to the underlying common data set, changes toparticular presentation areas can be matched or connected to aparticular reference point or range of reference points, which can thenbe connected to the other presentation areas.

The time slider presentation area allows users to slide or adjust eachend of an interactive time slider to view portions of time within aparticular manufacturing process session. In some instances, as the userinteracts with the slider (which may be of any type or format, includinganalog or digital), a tool tip or other indication can display the exacttimes at which the slider is positioned. When a particular period oftime is selected, the period is matched to the other presentation areasto automatically update and select information from those presentationareas corresponding to the selected time period. In some instances, the3D visualization may be rendered and/or zoomed to reflect the portionsof the machine path traveled during the selected time period, while theset of instructions selection box presents or otherwise distinguishesthe particular instructions executed during the selected time period.Any presented 2D metric charts may be updated to limit theirpresentation to the selected time period, or may highlight or otherwiseemphasize the values associated with the time period.

The set of instructions selection area provides an interactive listingof the particular instructions provided to or executed by the machineduring the manufacturing process session. The set of instructionsselection area allows users to click on or select an instruction orselected set of (sequential or non-sequential) instructions to focusupon. The selected instructions may be associated with a particularaction or a particular time at which the instructions were executed.Based on the selection, a corresponding reference value linking theinstructions to the common underlying data set can be identified (e.g.,based on an explicit timing, distance traveled, action performed, etc.).Data in the 3D visualization and the 2D metrics can be updated tocorrespond to (e.g., to emphasize the related data or to focus thepresentation on) information within the presentation area correspondingto the reference values of the selected instructions. The time slidermay also be updated to correspond to the times or range of timesassociated with the selected instructions.

The 3D visualization presentation area provides an illustration of thepath traveled by the machine during the manufacturing process session(e.g., an actual path traveled or a path as expected to be traveledbased on the instructions executed by the machine). In some instances, aCAD-based (or other) image of the workpiece being manufactured can bepresented in the 3D visualization showing how the path relates to themanufactured workpiece. By interacting with the 3D visualization, userscan identify particular portions of the path traveled, or particularportions of the workpiece, for further investigation or information. Insome instances, zooming in the 3D visualization may trigger an update tothe other presentation areas based on the area zoomed in on (e.g.,identifying the distances traveled and/or the time period associatedwith the paths traveled). In some instances, a selection interaction mayselect a radial selection associated with an area of the workpiece. Insuch a selection, non-sequential sets of path data may be selected, suchthat the ranges of reference points are also not sequential. In thoseinstances, non-sequential sets of corresponding data in otherpresentation areas may be selected or presented matching those selectedin the 3D visualization.

As previously described, the systems herein may identify one or morecritical regions based on various thresholds manually defined by users,dynamically defined as data is received, and set as defaults within thesystem. In some instances, the thresholds may be evaluated in relationto a particular metric, such as a contouring error, calculated based ona comparison of an actual and expected path of the machine. In otherinstances, particular characteristics or measured values or calculatedvalues associated with the execution of the machine may define when acritical region is detected. Because the presentation areas areconnected to the common underlying data set, a critical area determinedin a particular presentation area or associated with a particular dataset included in the presentation area can be visualized in one or moreof the other presentation areas. For example, where a contouring errorexceeds a particular threshold, resulting in a critical region that ispresented in the 2D metric chart region, the critical region can beidentified for a particular range of time, a distance traveled, or atindividual locations where the critical region occurs. Those criticalregions can be identified and matched to a group of data points, whichcan then be matched to one or more reference values associated with thecommon underlying data set. Based on those reference values, thecorresponding portions of the other presentation areas can be identifiedwhich correspond to the identified critical regions, thereby providinginformation to the user about which parts of the other presentations arepart of those critical regions.

Returning to FIG. 8, at 805 a machine-related data set associated with amanufacturing process session performed by a machine (e.g., physicalmachine 370) is presented. The machine-related data-set represents acommon underlying data set describing the manufacturing process andpresented in a plurality of presentation areas, each presentation areaproviding a different view associated with the machine-related data set.As previously described, the common underlying data set of themachine-related data set may be collected from various sources,including the physical machine, sensors monitoring or associated withthe manufacturing process, and/or one or more data sources. Portions ofthe data may be stored together or separately, but is otherwise linkedor provided reference to some or all of the other related data, such asby a time- or location-based reference point. For example, differentpresentations of the data may be associated with different dataconnected to the manufacturing process, such as a path traveled by themachine (e.g., an actual path and/or a path expected to be traveled by atool or end-effector of the physical machine based on the instructionsexecuted by the machine, or both), a set of instructions executed by themachine, one or more metrics associated with the manufacturing processsession (e.g., monitored metrics/parameters and/or calculated or derivedmetrics based on the monitored data), and a time slider associated witha reference time value of the session. As described, the data presentedin each of the presentation areas can be connected to themachine-related data based on anchor or reference points, such as adistance traveled or a particular time associated with the data.

At 810, a selection of a particular group of data points within aparticular one of the presentation areas is identified. The selectionmay be of a particular spatial area in a 3D visualization of the pathtraveled, a particular instruction or set of instructions, a particularportion of a 2D metric, or a particular range in a time slider, amongothers. At 815, based on the selected group of data points, theparticular presentation area is updated, if necessary, based on theselected group of data points. In some instances, the update may includea modification to the presentation, including an indication of aparticular selection (e.g., highlighting or other distinction of theselected group of data points) or a zooming in or other resizing of datawithin the presentation area.

At 820, reference values associated with the machine-related data setare identified based on the selected group of data points. The referencevalues may be any particular value shared or linking the various datasets in the presentation areas, including time values associated withthe selected group of data points, particular locations or distancestraveled associated with the data points, or another suitable referencevalue. By identifying the reference values, corresponding groups of datain the other presentation areas may be located or identified.

Operations 825 and 830 are performed for each of the other presentationareas in the overall presentation. At 825, particular sets of dataincluded in each of the other presentation areas corresponding to theidentified reference values are identified. At 830, each of thepresentation areas are updated based on the identified particular setsof data corresponding to the identified reference values. In otherwords, groups of data points in each of the other presentation areas aredetermined based on their underlying link or associated connection tothe initially selected group of data points.

In some instances, the operations of FIG. 8 may be performed based on aselection of a particular critical region illustrated in a particularvisualization within one or more of the presentation areas. In suchinstances, a user interaction with a particular critical regionindication (e.g., a highlighted portion of the data associated with thecritical region) can initiate the selection and updating process. Inothers, a determination that a critical region or regions exist in onepresentation area can automatically initiate the operations of method800.

FIG. 9 illustrates an example process for performing a pattern-basedbenchmarking process based on various metrics measured and/or calculatedduring various manufacturing process sessions. For clarity ofpresentation, the description that follows generally describes method900 in the context of the system 100 illustrated in FIGS. 1-3. However,it will be understood that method 900 may be performed, for example, byany other suitable system, environment, software, and hardware, or acombination of systems, environments, software, and hardware asappropriate.

Pattern-based benchmarking allows users (or the system, performing theprocess automatically without user interaction) to compare, relatively,a plurality of sessions associated with similar workpiece processes. Insome instances, the sessions may be associated with a single machine orcombination of machines performing a particular manufacturing process invarious sessions. In some instances, the sessions may be performed bydifferent machines each performing a particular manufacturing processrelated to the same workpiece. In other instances, the sessions may beperformed by machines performing different processes related to the sameor different workpieces. Pattern-based benchmarking allows users tovisually compare data from multiple sessions of production and, in somecases, to identify ideal or preferred configurations of parametersand/or processes based on the comparison. Further, patterns of errorsmay be identified, as well as ideal sessions to emulate or otherwiselearn from as relatively better for future manufacturing processes.

Specifically, pattern-based benchmarking allows users to comparemultiple sessions of production side-by-side or top-to-bottom, to easilyidentify patterns of error. The design visualizes data from each sessionin the form of a related domain, such as timeline or tool traveldistance for the user to analyze in comparison. If critical regions areassociated with portions of particular sessions, that information may bepresented within the presentation. Sessions with fewer critical regionsmay be preferred baseline sessions, and can be used to compare to othersessions associated with additional critical regions. Critical regionscan be presented in a distinguished or highlighted manner across thesessions for easy and fast comparison, and information about particularsessions can be identified to determine what may be causing the criticalregions to occur.

A plurality of sessions may be provided, where each session has beenevaluated for variations of particular metric. Users are able to selecta particular session from the plurality of sessions as the baselinesession, and a comparison for at least some of the other session in theplurality is generated relative to the selected session. In someinstances, in response to setting a particular session as the baselinesession, additional information specific to the selected session may bepresented, e.g., as a pop-up window or in a details area. In someinstances, information about other parameters associated with thesession may be condensed or shown via alternative indicators, such aserrors, critical regions, and other areas of interest. Other sessionsmay be presented in a reduced or partial manner, with areas of interestshown via reduced or alternative indicators as well. By selecting aparticular one of the other sessions, users may expand particularsession's timeline (or other suitable axis) in an accordion-like orexpanding manner to access more detailed information regarding thatsession, for example, specific values or illustrations of a line graphassociated with the data, details about particular areas where criticalregion thresholds are exceeding, time and/or distance traveledinformation, as well as other information.

Interactions with the baseline session may also be propagated through tothe others in the plurality of sessions. The pattern-based benchmarkingcan be used to compare execution as compared to particular thresholdsthat have been set in connection with at least one of the session data.For example, if a particular contouring error threshold is set in afirst session selected as the benchmark baseline, the threshold can bepropagated down to the remaining sessions and used to evaluate arelative execution such that the threshold is reflected in each of theplurality of sessions.

Turning to FIG. 9, a plurality of metrics associated with machineexecution that are generated during a plurality of manufacturing processsessions may be available for selection or further analytical review.The metrics may be a measured parameter, input, or output monitoredduring a particular session, or may be a calculated or otherwise derivedvalue generated based on one or more parameters, inputs, or outputsmonitored during the particular session. Examples of metrics may includevelocity of the machine (or a component thereof) during the session, acontouring error at a particular time or distance traveled, atemperature level of a particular component or tool during the session,sound emitted during the session, or any other suitable metric evaluatedduring at least some of the sessions. The metrics may be associated within a two-dimensional presentation, where the X-axis of the presentationrepresents a time during the session or a distance traveled during thesession and where the Y-axis of the presentation represents a value(absolute or relative) of the particular metric. At 905, a particularmetric from the plurality of metrics may be identified, where at least aportion of the plurality of sessions are associated with the particularmetric or have had the particular metric measured, monitored,calculated, or otherwise evaluated. In some instances, not all sessionsmay be associated with a particular metric, such as where a traceconfiguration applied at the time does not include the particularmetric.

At 910, a plurality of sessions associated with the manufacturingprocess and associated with the identified metric are presented. In someinstances, the sessions may be presented in a new presentation area orwindow with two or more panes or areas. In other instances, theplurality of sessions may be presented in one presentation area of alarger presentation area that includes 3D visualizations associated witha manufacturing process, a time slider, and/or other visualizations. Insome instances, one or more of the sessions may represent an average orcombination of one or more other sessions. For example, a particularsession may represent an average of all sessions associated with themetric, such that the values within the particular represent an averagevalue at each time or location throughout the metric.

At 915, a selection of a particular session from the plurality ofsessions is identified, where the selection results in the particularsession being used as a baseline session. The particular session may beselected based on user input or may be automatically identified based onan evaluated relative quality or inferiority. In some instances, anaverage or median session may be calculated and automatically selectedas the baseline session. In other instances, an evaluated best or worstsession relative to the identified metric may be determined andautomatically selected as the baseline session.

In response to the selection of the particular session as the baselinesession, several operations are performed. At 920, the selectedparticular session can be presented in a primary portion of thepresentation area, such as a top area of the presentation area reservedfor a baseline session's presentation. At 925, the remaining sessions inthe plurality of sessions associated with the metric can be normalizedbased on a characteristic of the selected particular session. Forexample, the selected particular session may have taken a particularlength of time to complete. The other sessions associated with themetric may have taken a longer or shorter time to complete. To comparethose times, a normalization process may be performed to compress orexpand the presentation of those other processes, as necessary, to matchthe selected particular session's length of time. In addition to time,for similar processes being performed, a distance traveled may be usedto normalize the remaining sessions. Normalization may also be based onspatial reference to the workpiece, among others. In otherimplementations, normalization may be optional and/or triggered via userrequest. If one or more of the other sessions are completed in shorteror longer periods than the particular selected session, the differencesin time periods may remain when the other sessions are presented.Normalization may be performed on the x-axis of the 2D metric or on they-axis of the 2D metric, as appropriate.

At 930, at least a portion of the normalized remaining sessions arepresented in a secondary portion of the presentation area. The secondaryportion of the presentation area may be dedicated area below or near theprimary portion of the presentation area. In some instances, thepresentation of the particular selected session may be provided in afirst level of detail, while the one or more other sessions presented inthe secondary area are provided in a second level of detail, the secondlevel of detail relatively lower than the first level of detail. Forexample, the primary portion of the presentation area may provide alarger presentation of the data with additional points of interest anddata, additional indices or indications of values, and, in someinstances, additional information or context. The secondary portion ofthe presentation area may present a condensed or reduced sizeillustration of the presented remaining sessions.

In some instances, one or more critical region thresholds may be definedfor the baseline session, or for each of the sessions associated withthe identified metric. The critical region threshold may defineparticular values of the identified metric where, if the value of themetric exceeds (or, if appropriate, falls below) the threshold, acritical region is identified. In some instances, multiple thresholdsmay be included or part of each evaluation. The critical region, asdescribed, may be associated with potential issues with the process, themachine, and/or the workpiece being manufactured. If one or morethresholds are defined for the baseline session, the thresholds may alsobe propagated to and used to assess critical regions for each of thepresented remaining sessions, such that the presentations of theremaining sessions include an indication of one or more criticalregions.

The process of selecting a particular baseline session can be dynamicand iterative. As such, at 935 a determination is made as to whether anew session from the plurality of sessions associated with theidentified metric is identified as a new baseline session. Any number ofiterations may be performed, allowing for consideration and testing ofdifferent sessions as the baseline session for a particular metric. If anew session is identified as the new baseline, method 900 can performoperations 940 to 950 in response. At 940, the selected new session ispresented in the primary portion of the presentation area. At 945, theother sessions associated with the identified metric can be normalizedin a similar manner to the operations of 925. At 950, the normalizedremaining sessions are presented in the secondary portion of thepresentation area. Returning to 935, if it is determined that a newsession is not identified, method 900 can return to 905, where a newmetric associated with the manufacturing process can be identified.

In some instances, the plurality of sessions compared based on anidentified metric may be based on sessions performed on differentmachines. For example, the same set of instructions may be provided totwo or more different machines of the same model, such as in a factorysetting. In theory, the operations performed by the two or moredifferent machines should be similar based on a common set ofinstructions being performed. However, differences may exist based ondifferences in the actual operation and/or one or more external factors.Therefore, sessions associated with the different machines can becompared via the pattern-based benchmarking process. In some instances,sessions associated with particular machines may be identified ordistinguished when presented allowing for quick user knowledge as towhich sessions apply to which machine.

In some instances, the plurality of sessions compared based on theidentified metric may be based on the same or different machinesperforming different instructions sets (e.g., different processes) togenerate a similar underlying workpiece. In those instances, theoperations may cause significant differences in particular metricvalues, or at least different values at different times or alongdifferent portions of a distance or path traveled by the machine. Insuch instances, while a direct comparison along a particular domain,such as travel distance, may be difficult, a comparison of a number orseverity of critical regions associated with the different sessions canbe used to compare particular sessions and therefore procedures.

FIGS. 10-14D show example presentations and screenshots of various UIsand/or portions of UIs associated with the described processes. Theseillustrations are for example purposes, with any number of alternativeimplementations being available to present the information regardingmanufacturing processes.

FIG. 10 presents an example view of a user interface 1000 where a 3Dvisualization is presented. In UI 1000, a 3D canvas 1005 is presentedproviding a visualization of a path (either actual or expected based onthe executed instructions) taken by a particular machine, tool, or robot(or a part or component thereof) during the execution of the monitoredmanufacturing process.

As illustrated, the UI 1000 includes the 3D canvas 1005, a time slider1025, a tab providing additional information or specific locations ofone or more critical regions 1010 (shown as not activated), a tab 1015providing a presentation of the underlying set of instructions 1020executed during the manufacturing process (shown as activated), and apresentation area 1050 of a 2D metric measured or evaluated over thedata obtained during the course of the process. The 3D canvas 1005includes a CAD illustration of the workpiece 1035 being manufactured.The CAD illustration 1035 may be optional or toggle-able, and providescontext to the operations of the machine as presented or visualized viathe illustrated path 1030. The emphasized portions of the path 1040represent locations and/or times during the manufacturing process wherea critical region is detected or calculated. As described, the criticalregions may be determined based on a default, user-configured, ordynamically-determined value threshold associated with one or acombination of measured or calculated metrics. If the value of themetric(s) exceeds a certain value or if the conditions defining acritical error are met, then the system can identify those parts asbeing a critical region. As shown in the points/regions 1040, anillustration of where on the path the critical regions are calculatedcan be shown with a highlighted or otherwise distinguished indication.Similarly, an indication such as the distinguishing of particular code1045 in the set of instructions 1020 can be presented to indicate theinstructions associated with a critical region.

The presentation area 1050 of the 2D metric includes an illustration ofthe metric values over the time during which the process was performedin the illustrated UI 1000. In other instances, the X-axis may be basedon a distance traveled during the manufacturing process, a percentage ofprogress through the process, a particular spatial location, or anyother suitable indication of a place in the manufacturing process. Theline 1055 represents a determined maximum threshold for the metric. Asillustrated, areas above the line may be evaluated as critical regions,with that information being shared to and presented in the otherportions of the UI. The portion 1060 of the graphed metric valuesindicates a particular portion of the values exceeding the particularcritical region threshold in the illustration. Thresholds may be staticor dynamic, and be maximum or minimum thresholds, existing individuallyor as a combination of thresholds.

Time slider 1025 can be used to specify a particular portion of themanufacturing process session that has been traced to be illustrated,and can provide immediate information as to which portion of the sessionis being presented. In the current illustration, the entirety of theprocess session is being presented.

FIG. 11 presents an illustration of a zoomed in portion of the 3Dvisualization presented in FIG. 10. In particular, the illustratedrelates to a smaller section of the path 1130 taken by the tool andpresented in the 3D canvas 1105. The selection may be made by narrowingthe time slider 1125 between two times (here, between 4.3 and 6.9seconds) within the overall time of the process, selecting or zooming inon a particular point or points on the presentation, selecting aparticular set of instructions, or identifying a part of the 2D metricto view, among others. If one of the selections above is made, the otherportions, using, for example, the operations described above related tothe four-way interactions, selections in one or more of the otherportions can be automatically made that correspond to the initialselection. As illustrated, the initial selection may be in a particulartime range on the time slider 1125, where the relevant portions of theset of instructions 1120 has been selected/highlighted (portion 1142),the 2D presentation area 1150 has been updated to match the timesindicated, and the visualization 1130 of the path (and the portion ofthe associated workpiece) have been zoomed to the appropriate regions inresponse to the time slider 1125 selection.

An indication has been made to request or cause a portion of the 2Dmetric data in the 2D metric presentation area 1150 to be presented ontop of and in connection with the presentation of the path 1130visualized. One or more critical regions 1135 have been illustrated asdistinguished entries or portions on the path 1130, and the 2Dpresentation selected has been added to the 3D canvas 1105. Asillustrated, the portion 1140 of the 2D graph incorporated into the 3Dcanvas 1105 is provided at a particular angle to allow for ease ofviewing. In some instances, color coding or other distinguishingcharacteristics may be included to show where particular criticalregions are on the 2D presentation portion 1140. In some instances, theactual values may be scaled to allow for a more readable presentation,as well. If the view in the 3D canvas is rotated, the 2D metricpresentation portion 1140 may be rotated in connection, or may berepositioned to allow for a readable view of the data.

FIGS. 12 (FIGS. 12A-B) and 13 (FIGS. 13A-B) illustrate exampleinteractions provided by the four-way interaction solution describedherein. As illustrated in FIG. 12, a UI 1200 includes multiplepresentation areas, including a presentation area 1215 for a 3Dvisualization of a path traveled by a tool or end effector inmanufacturing a workpiece, a time slider 1205, a presentation area 1210associated with the set of instructions executed during a particularmanufacturing session, and a presentation area 1220 associated with oneor more 2D presentations of evaluated metrics. Each of the presentationareas and time slider 1205 represent or provide UI controls that can bemanipulated by a user to select a particular range or set of data withina particular one of the presentation areas. The various presentationsare associated via a common underlying connection or reference betweenthe machine-related data set, wherein each of the presentation areaspresent a view upon that common data set. When a particular one of theareas is modified (e.g., by a selection of a range, by a selection ofparticular instruction or set of instructions, by zooming of aparticular chart, etc.), an operation is automatically performed toidentify the reference points (e.g., times, spatial locations, distancestraveled during the manufacturing process, etc.) associated with theselection. Those reference points are then identified in or mapped tothe other presentation areas and their associated data views, with thosepresentations being updated to match or correspond to the referencepoints identified after the UI interaction in the first presentationarea.

FIG. 13 shows the updated UI 1300 and presentation areas after a reducedrange of time is selected via the time slider 1205. The updated timeslider 1305 shows the revised time range as selected during a UIinteraction. The presentation area 1315 presents the 3D visualization ofthe path associated with the selected time range (and zoomed in to showthe operations of that time period more clearly), while the presentationarea 1320 presents the illustrated 2D metric during the time periodspecified. Similarly, the presentation area 1310 associated with the setof instructions presents the portions of the instructions specificallyexecuted or otherwise associated with the selected time range.Additionally, in this instance, a set of critical regions occurringwithin the time range are presented.

FIGS. 14A-14D illustrate example visualizations and interactionsprovided by the pattern-based benchmarking solution described herein. Inthe illustration, the 2D metric presentation area is included within itsown UI 1400. In some instances, the operations and interactionsillustrated may be provided within a UI similar to the previouslyillustrated UIs, such as UI 1000, 1200, 1300, where the multiplepresentation areas are provided. Alternatively, such as when users areinvestigating differences between different sessions, the 2D metricpresentation area may be provided alone in a UI (e.g., UI 1400), or maybe made more prominent within one of the other UIs.

In FIG. 14A, a user can initially be presented with a presentation thatincludes a baseline session presentation area 1410, a region ofinterest/instruction area 1420, and a session area 1425. A plurality ofsessions 1430 (or at least two) may be presented in association with aparticular manufacturing process, where the plurality of sessions 1430are monitored executions at the same machine at different times, at thesame and/or different machines using a similar manufacturing process atthe same or different times, or at the same and/or different machinesusing one or more different manufacturing processes at the same ordifferent times. Each of the sessions may be selectable control allowinginteraction with and/or selection of the particular session. In theillustrated example, a drag-and-drop interaction may be used by users toselect a particular session as a baseline session for evaluationpurposes. Any other suitable interactions may be used to select aparticular session as the baseline session, including double-clicking ortapping actions, voice-activated selections, text input, and others.Here, the single session 1440 may be initially determined to be thefirst baseline session to be used. In some instances, a particularsession may be automatically selected (e.g., based on fewest criticalregions, previous indication as baseline session, etc.), or theparticular session considered a baseline session may be combined,averaged, or otherwise calculated session based on some or all of theplurality of sessions 1430 (or other suitable sessions outside of theplurality of sessions 1430).

FIG. 14B illustrates the particular session 1440 beingdragged-and-dropped into the baseline session presentation area 1410. Anillustration of the selected session 1440 may be retained within thesession area 1425 or it may be visually removed.

FIG. 14C illustrates UI 1400 after the selection is complete. Asillustrated, a detailed view of the selected session 1440 can bepresented in the baseline session presentation area 1410. In addition,additional detail about the selected session 1440 may be presented inthe optional region of interest/instruction area 1420. The presentationassociated with each of the plurality of sessions 1430 may be updatedbased on characteristics of the selected baseline session. For example,the other sessions 1430 may be normalized to a size or distance traveledof the baseline session 1440, where a compressing or expanding of thepresentation may occur. A control 1450 for expanding the plurality ofsession 1430 and providing detailed views of the metric is presented. Asimilar control for collapsing sessions is also provided. In someinstances, interactions with individual session of the plurality ofsessions 1430 can result in a single session being expanded/collapsed atthe user's instruction. FIG. 14D provides an illustration of theexpanded sessions, and can allow users to compare the metrics over timebetween the baseline session 1440 and the one or more sessions 1430.

The illustrated interactions present a single iteration of thepattern-based benchmarking process. Multiple iterations may be performedby selecting (e.g., dragging-and-dropping) a different session into thebaseline session presentation area 1410, which may be performed at anysuitable time. The remaining sessions can be re-evaluated in responseand presented in the session area 1425 for further analysis.

Implementations of the subject matter and the functional operationsdescribed in this specification can be implemented in digital electroniccircuitry, in tangibly embodied computer software or firmware, incomputer hardware, including the structures disclosed in thisspecification and their structural equivalents, or in combinations ofone or more of them. Implementations of the subject matter described inthis specification can be implemented as one or more computer programs,that is, one or more modules of computer program instructions encoded ona tangible, non-transitory, computer-readable computer-storage mediumfor execution by, or to control the operation of, data processingapparatus. Alternatively, or additionally, the program instructions canbe encoded in/on an artificially generated propagated signal, forexample, a machine-generated electrical, optical, or electromagneticsignal that is generated to encode information for transmission tosuitable receiver apparatus for execution by a data processingapparatus. The computer-storage medium can be a machine-readable storagedevice, a machine-readable storage substrate, a random or serial accessmemory device, or a combination of computer-storage mediums.

The terms “data processing apparatus,” “computer,” or “electroniccomputer device” (or equivalent as understood by one of ordinary skillin the art) refer to data processing hardware and encompass all kinds ofapparatus, devices, and machines for processing data, including by wayof example, a programmable processor, a computer, or multiple processorsor computers. The apparatus can also be or further include specialpurpose logic circuitry, for example, a central processing unit (CPU),an FPGA (field programmable gate array), or an ASIC(application-specific integrated circuit). In some implementations, thedata processing apparatus or special purpose logic circuitry (or acombination of the data processing apparatus or special purpose logiccircuitry) may be hardware- or software-based (or a combination of bothhardware- and software-based). The apparatus can optionally include codethat creates an execution environment for computer programs, forexample, code that constitutes processor firmware, a protocol stack, adatabase management system, an operating system, or a combination ofexecution environments. The present disclosure contemplates the use ofdata processing apparatuses with or without conventional operatingsystems, for example LINUX, UNIX, WINDOWS, MAC OS, ANDROID, IOS, or anyother suitable conventional operating system.

A computer program, which may also be referred to or described as aprogram, software, a software application, a module, a software module,a script, or code can be written in any form of programming language,including compiled or interpreted languages, or declarative orprocedural languages, and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, or other unitsuitable for use in a computing environment. A computer program may, butneed not, correspond to a file in a file system. A program can be storedin a portion of a file that holds other programs or data, for example,one or more scripts stored in a markup language document, in a singlefile dedicated to the program in question, or in multiple coordinatedfiles, for example, files that store one or more modules, sub-programs,or portions of code. A computer program can be deployed to be executedon one computer or on multiple computers that are located at one site ordistributed across multiple sites and interconnected by a communicationnetwork. While portions of the programs illustrated in the variousfigures are shown as individual modules that implement the variousfeatures and functionality through various objects, methods, or otherprocesses, the programs may instead include a number of sub-modules,third-party services, components, libraries, and such, as appropriate.Conversely, the features and functionality of various components can becombined into single components as appropriate. Thresholds used to makecomputational determinations can be statically, dynamically, or bothstatically and dynamically determined.

The methods, processes, logic flows, etc. described in thisspecification can be performed by one or more programmable computersexecuting one or more computer programs to perform functions byoperating on input data and generating output. The methods, processes,logic flows, etc. can also be performed by, and apparatus can also beimplemented as, special purpose logic circuitry, for example, a CPU, anFPGA, or an ASIC.

Computers suitable for the execution of a computer program can be basedon general or special purpose microprocessors, both, or any other kindof CPU. Generally, a CPU will receive instructions and data from aread-only memory (ROM) or a random access memory (RAM), or both. Theessential elements of a computer are a CPU, for performing or executinginstructions, and one or more memory devices for storing instructionsand data. Generally, a computer will also include, or be operativelycoupled to, receive data from or transfer data to, or both, one or moremass storage devices for storing data, for example, magnetic,magneto-optical disks, or optical disks. However, a computer need nothave such devices. Moreover, a computer can be embedded in anotherdevice, for example, a mobile telephone, a personal digital assistant(PDA), a mobile audio or video player, a game console, a globalpositioning system (GPS) receiver, or a portable storage device, forexample, a universal serial bus (USB) flash drive, to name just a few.

Computer-readable media (transitory or non-transitory, as appropriate)suitable for storing computer program instructions and data include allforms of non-volatile memory, media and memory devices, including by wayof example semiconductor memory devices, for example, erasableprogrammable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), and flash memory devices;magnetic disks, for example, internal hard disks or removable disks;magneto-optical disks; and CD-ROM, DVD+/−R, DVD-RAM, and DVD-ROM disks.

The memory may store various objects or data, including caches, classes,frameworks, applications, backup data, jobs, web pages, web pagetemplates, database tables, repositories storing dynamic information,and any other appropriate information including any parameters,variables, algorithms, instructions, rules, constraints, or referencesthereto. Additionally, the memory may include any other appropriatedata, such as logs, policies, security or access data, reporting files,as well as others. The processor and the memory can be supplemented by,or incorporated in, special purpose logic circuitry.

To provide for interaction with a user, implementations of the subjectmatter described in this specification can be implemented on a computerhaving a display device, for example, a CRT (cathode ray tube), LCD(liquid crystal display), LED (Light Emitting Diode), or plasma monitor,for displaying information to the user and a keyboard and a pointingdevice, for example, a mouse, trackball, or trackpad by which the usercan provide input to the computer. Input may also be provided to thecomputer using a touchscreen, such as a tablet computer surface withpressure sensitivity, a multi-touch screen using capacitive or electricsensing, or other type of touchscreen. Other kinds of devices can beused to provide for interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback, forexample, visual feedback, auditory feedback, or tactile feedback; andinput from the user can be received in any form, including acoustic,speech, or tactile input. In addition, a computer can interact with auser by sending documents to and receiving documents from a device thatis used by the user; for example, by sending web pages to a web browseron a user's client device in response to requests received from the webbrowser.

The term “graphical user interface,” or “GUI,” may be used in thesingular or the plural to describe one or more graphical user interfacesand each of the displays of a particular graphical user interface.Therefore, a GUI may represent any graphical user interface, includingbut not limited to, a web browser, a touch screen, or a command lineinterface (CLI) that processes information and efficiently presents theinformation results to the user. In general, a GUI may include aplurality of user interface (UI) elements, some or all associated with aweb browser, such as interactive fields, pull-down lists, and buttons.These and other UI elements may be related to or represent the functionsof the web browser.

Implementations of the subject matter described in this specificationcan be implemented in a computing system that includes a back-endcomponent, for example, as a data server, or that includes a middlewarecomponent, for example, an application server, or that includes afront-end component, for example, a client computer having a graphicaluser interface or a Web browser through which a user can interact withan implementation of the subject matter described in this specification,or any combination of one or more such back-end, middleware, orfront-end components. The components of the system can be interconnectedby any form or medium of wireline or wireless digital data communication(or a combination of data communication), for example, a communicationnetwork. Examples of communication networks include a local area network(LAN), a radio access network (RAN), a metropolitan area network (MAN),a wide area network (WAN), Worldwide Interoperability for MicrowaveAccess (WIMAX), a wireless local area network (WLAN) using, for example,802.11 a/b/g/n or 802.20 (or a combination of 802.11x and 802.20 orother protocols consistent with this disclosure), all or a portion ofthe Internet, or any other communication system or systems at one ormore locations (or a combination of communication networks). The networkmay communicate with, for example, Internet Protocol (IP) packets, FrameRelay frames, Asynchronous Transfer Mode (ATM) cells, voice, video,data, or other suitable information (or a combination of communicationtypes) between network addresses.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anyinvention or on the scope of what may be claimed, but rather asdescriptions of features that may be specific to particularimplementations of particular inventions. Certain features that aredescribed in this specification in the context of separateimplementations can also be implemented, in combination, in a singleimplementation. Conversely, various features that are described in thecontext of a single implementation can also be implemented in multipleimplementations, separately, or in any suitable sub-combination.Moreover, although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can, in some cases, be excised from thecombination, and the claimed combination may be directed to asub-combination or variation of a sub-combination.

Particular implementations of the subject matter have been described.Other implementations, alterations, and permutations of the describedimplementations are within the scope of the following claims as will beapparent to those skilled in the art. While operations are depicted inthe drawings or claims in a particular order, this should not beunderstood as requiring that such operations be performed in theparticular order shown or in sequential order, or that all illustratedoperations be performed (some operations may be considered optional), toachieve desirable results. In certain circumstances, multitasking orparallel processing (or a combination of multitasking and parallelprocessing) may be advantageous and performed as deemed appropriate.

Moreover, the separation or integration of various system modules andcomponents in the implementations described above should not beunderstood as requiring such separation or integration in allimplementations, and it should be understood that the described programcomponents and systems can generally be integrated together in a singlesoftware product or packaged into multiple software products.

Accordingly, the above description of example implementations does notdefine or constrain this disclosure. Other changes, substitutions, andalterations are also possible without departing from the spirit andscope of this disclosure.

Furthermore, any claimed implementation below is considered to beapplicable to at least a computer-implemented method; a non-transitory,computer-readable medium storing computer-readable instructions toperform the computer-implemented method; and a computer systemcomprising a computer memory interoperably coupled with a hardwareprocessor configured to perform the computer-implemented method or theinstructions stored on the non-transitory, computer-readable medium.

The preceding figures and accompanying description illustrate exampleprocesses and computer implementable techniques. But environment (or itssoftware or other components) contemplates using, implementing, orexecuting any suitable technique for performing these and other tasks.It will be understood that these processes are for illustration purposesonly and that the described or similar techniques may be performed atany appropriate time, including concurrently, individually, or incombination. In addition, many of the steps in these processes may takeplace simultaneously, concurrently, and/or in different orders than asshown. Moreover, environment 100 may use processes with additionalsteps, fewer steps, and/or different steps, so long as the methodsremain appropriate.

In other words, although this disclosure has been described in terms ofcertain embodiments and generally associated methods, alterations andpermutations of these embodiments and methods will be apparent to thoseskilled in the art. Accordingly, the above description of exampleembodiments does not define or constrain this disclosure. Other changes,substitutions, and alterations are also possible without departing fromthe spirit and scope of this disclosure.

What is claimed is:
 1. A computer-implemented method performed by atleast one processor executing instructions, the method comprising:identifying a particular metric associated with execution of amanufacturing process, the particular metric evaluated in a plurality ofmanufacturing process sessions for at least one manufacturing process;identifying a particular session from the plurality of manufacturingprocess sessions as a baseline session, wherein at least a portion ofthe remaining plurality of manufacturing process sessions are to becompared to the baseline session; presenting, in a primary portion of apresentation area, a visualization of the values of the identifiedmetric associated with the identified particular session; andpresenting, in a secondary portion of the presentation area,visualizations of the values of the identified metric associated with atleast a portion of the other manufacturing process sessions from theplurality of sessions.
 2. The method of claim 1, wherein in response toidentifying the particular session from the plurality of manufacturingprocess sessions as a baseline session, at least one of the othermanufacturing process sessions are automatically normalized to thebaseline session based on a characteristic of the identified particularsession.
 3. The method of claim 2, wherein the characteristic of theidentified particular session comprises a time period over which theidentified particular session was performed.
 4. The method of claim 3,wherein normalizing the at least one of the other manufacturing processsessions comprises compressing or expanding visualizations of the atleast one of the other manufacturing process session over the timeperiod of the identified particular session, where the at least one ofthe other manufacturing process sessions was performed over a differenttime period than the identified particular session.
 5. The method ofclaim 2, wherein the characteristic of the identified particular sessioncomprises a traveled distance of the machine performing themanufacturing process.
 6. The method of claim 1, wherein the identifyingthe particular session from the plurality of manufacturing processsessions as a baseline session comprises: initially presenting at leasta portion of the plurality of manufacturing process sessions via a userinterface; and receiving, via the user interface, a selection of theparticular session by a user associated with the user interface.
 7. Themethod of claim 6, the method further comprising: receiving, via theuser interface, a selection of a new session from the presented portionof the other manufacturing process sessions as a new baseline session;and in response to receiving the selection of the new session:presenting, in the primary portion of a presentation area, avisualization of the values of the identified metric associated with theselected new session; and presenting, in the secondary portion of thepresentation area, visualizations of the values of the identified metricassociated with at least a portion of the other manufacturing processsessions from the plurality of sessions.
 8. The method of claim 1,wherein the visualization presented in the primary portion of thepresentation area is at a first level of detail and the visualizationspresented in the secondary portion of the presentation area are at asecond level of detail, wherein the first level of detail is arelatively higher level of detail than the second level of detail. 9.The method of claim 1, wherein at least a portion of manufacturingprocess sessions in the plurality of manufacturing process sessions areperformed on different machines performing a common manufacturingprocess to machine a workpiece.
 10. The method of claim 1, wherein atleast a portion of manufacturing process sessions in the plurality ofmanufacturing process sessions are performed on different machinesperforming different manufacturing processes, where the differentmanufacturing processes each machine a common workpiece.
 11. The methodof claim 1, wherein initially the identified particular session isautomatically identified, where the identified particular sessioncomprises a simulated session based on averaged metric values of atleast a portion of the plurality of manufacturing process sessions. 12.A system comprising: at least one processor; and a memorycommunicatively coupled to the at least one processor, the memorystoring instructions which, when executed, cause the at least oneprocessor to perform operations comprising: identifying a particularmetric associated with execution of a manufacturing process, theparticular metric evaluated in a plurality of manufacturing processsessions for at least one manufacturing process; identifying aparticular session from the plurality of manufacturing process sessionsas a baseline session, wherein at least a portion of the remainingplurality of manufacturing process sessions are to be compared to thebaseline session; presenting, in a primary portion of a presentationarea, a visualization of the values of the identified metric associatedwith the identified particular session; and presenting, in a secondaryportion of the presentation area, visualizations of the values of theidentified metric associated with at least a portion of the othermanufacturing process sessions from the plurality of sessions.
 13. Thesystem of claim 12, wherein in response to identifying the particularsession from the plurality of manufacturing process sessions as abaseline session, at least one of the other manufacturing processsessions are automatically normalized to the baseline session based on acharacteristic of the identified particular session.
 14. The system ofclaim 13, wherein the characteristic of the identified particularsession comprises a time period over which the identified particularsession was performed.
 15. The system of claim 14, wherein normalizingthe at least one of the other manufacturing process sessions comprisescompressing or expanding visualizations of the at least one of the othermanufacturing process session over the time period of the identifiedparticular session, where the at least one of the other manufacturingprocess sessions was performed over a different time period than theidentified particular session.
 16. The system of claim 13, wherein thecharacteristic of the identified particular session comprises a traveleddistance of the machine performing the manufacturing process.
 17. Thesystem of claim 12, wherein identifying the particular session from theplurality of manufacturing process sessions as a baseline sessioncomprises: initially presenting at least a portion of the plurality ofmanufacturing process sessions via a user interface; and receiving, viathe user interface, a selection of the particular session by a userassociated with the user interface.
 18. The system of claim 17, theoperations further comprising: receiving, via the user interface, aselection of a new session from the presented portion of the othermanufacturing process sessions as a new baseline session; and inresponse to receiving the selection of the new session: presenting, inthe primary portion of a presentation area, a visualization of thevalues of the identified metric associated with the selected newsession; and presenting, in the secondary portion of the presentationarea, visualizations of the values of the identified metric associatedwith at least a portion of the other manufacturing process sessions fromthe plurality of sessions.
 19. The system of claim 12, wherein at leasta portion of manufacturing process sessions in the plurality ofmanufacturing process sessions are performed on different machinesperforming a common manufacturing process to machine a workpiece, orwherein at least a portion of manufacturing process sessions in theplurality of manufacturing process sessions are performed on differentmachines performing different manufacturing processes, where thedifferent manufacturing processes each machine a common workpiece.
 20. Anon-transitory computer-readable medium storing instructions which, whenexecuted, cause at least one processor to perform operations comprising:identifying a particular metric associated with execution of amanufacturing process, the particular metric evaluated in a plurality ofmanufacturing process sessions for at least one manufacturing process;identifying a particular session from the plurality of manufacturingprocess sessions as a baseline session, wherein at least a portion ofthe remaining plurality of manufacturing process sessions are to becompared to the baseline session; presenting, in a primary portion of apresentation area, a visualization of the values of the identifiedmetric associated with the identified particular session; andpresenting, in a secondary portion of the presentation area,visualizations of the values of the identified metric associated with atleast a portion of the other manufacturing process sessions from theplurality of sessions.